Genetics of Rare Autoimmune Diseases [1st ed.] 978-3-030-03933-2;978-3-030-03934-9

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Rare Diseases of the Immune System Series Editors: Lorenzo Emmi · Domenico Prisco

Javier Martín Francisco David Carmona Editors

Genetics of Rare Autoimmune Diseases

Rare Diseases of the Immune System Series Editors: Lorenzo Emmi Domenico Prisco Editorial Board: Systemic Vasculitis L. Emmi C. Salvarani R. A. Sinico

Primary Immunodeficiency A. Plebani C. T. Baldari M. M. D’Elios

Autoimmune Disease P. L. Meroni D. Roccatello M. Matucci Cerinic L. Emmi

Systemic Fibroinflammatory Disorders A. Vaglio

Autoinflammatory Syndromes M. Gattorno F. De Benedetti R. Cimaz

Pineta, Laura Maddii Emmi (Private collection)

Javier Martín  •  Francisco David Carmona Editors

Genetics of Rare Autoimmune Diseases

Editors Javier Martín Institute of Parasitology and Biomedicine Lopez-Neyra CSIC Granada Spain

Francisco David Carmona Department of Genetics University of Granada Armilla Granada Spain

ISSN 2282-6505     ISSN 2283-6403 (electronic) Rare Diseases of the Immune System ISBN 978-3-030-03933-2    ISBN 978-3-030-03934-9 (eBook) https://doi.org/10.1007/978-3-030-03934-9 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Autoimmunity is characterized by progressive tissue damage due to pathogenic processes whose primary cause is the loss of tolerance of the immune system against self-peptides of the organism. The clinical manifestations of such abnormal immune responses depend on the affected cells or tissues, resulting in a heterogeneous group of severe immune-mediated conditions known as autoimmune diseases. Autoimmune diseases have a relatively low prevalence individually, and, because of that, they are considered rare in most cases. Besides, most of them have a multifactorial etiology, in which a complex interaction between environmental and polygenic factors is responsible for their development and progression. As a consequence, these conditions received little attention during the last century in comparison with other more common diseases. However, the overall prevalence of autoimmunity has been estimated around 5–8% in Western countries which, together with their chronic and disabling nature in most cases, implies a considerable socioeconomic burden for both the patients and the healthcare systems. The increase in the sample size of the study cohorts and the use of novel technologies for genome-level data analysis during the last decade have substantially increased our knowledge of the molecular mechanisms involved in the susceptibility and expression of autoimmune diseases. It is currently known that common human genetic variation and epigenetics are directly involved in their pathophysiology. The elucidation of the genetic basis of autoimmunity is definitively helping in the identification of accurate diagnostic and prognostic markers as well as in the implementation of more effective personalized therapies. This volume is aimed at providing an updated overview of the recent progress gained on the molecular mechanisms that underlie the most relevant rare autoimmune diseases, providing a critical point of view about the possible future directions by basic and clinical researchers of worldwide renown. The two main reasons that motivated us to coordinate this fascinating initiative were the growing interest in this topic and the reduced number of documents related to it available in the literature.

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We would like to sincerely thank the authors that made possible the publication of this book, as we are confident that a broad range of students, practicing physicians, and scientists of related disciplines will benefit from their valuable knowledge on this field, which was acquired during many years of high effort, passion, and dedication. Granada, Spain Granada, Spain 

Javier Martín Francisco David Carmona

Contents

1 Systemic Lupus Erythematosus����������������������������������������������������������������   1 Susan K. Vester and Timothy J. Vyse 2 Systemic Sclerosis��������������������������������������������������������������������������������������  19 Elena López-Isac, Marialbert Acosta-Herrera, and Javier Martín 3 Behçet’s Disease������������������������������������������������������������������������������������������  37 Lourdes Ortiz-Fernández and Maria Francisca González-Escribano 4 Sjögren’s Syndrome ����������������������������������������������������������������������������������  53 Laëtitia Le Pottier, Kahina Amrouche, Amandine Charras, Anne Bordron, and Jacques-Olivier Pers 5 Polymyositis/Dermatomyositis������������������������������������������������������������������  95 Ana Márquez, Ernesto Trallero-Araguás, and Albert Selva-O’Callaghan 6 ANCA-Associated Vasculitis �������������������������������������������������������������������� 111 Francesco Bonatti, Alessia Adorni, Antonio Percesepe, Augusto Vaglio, and Davide Martorana 7 Giant Cell Arteritis������������������������������������������������������������������������������������ 129 Francisco David Carmona, Javier Martín, and Miguel A. González-Gay 8 Takayasu Arteritis�������������������������������������������������������������������������������������� 151 Elizabeth Gensterblum and Amr H. Sawalha 9 Primary Biliary Cirrhosis, Primary Sclerosing Cholangitis, and Autoimmune Hepatitis����������������������������������������������������������������������� 163 David González-Serna, Martin Kerick, and Javier Martín 10 Genetics of Multiple Sclerosis ������������������������������������������������������������������ 183 Antonio Alcina, Maria Fedetz, and Fuencisla Matesanz

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11 Autoimmune Myasthenia Gravis�������������������������������������������������������������� 203 Güher Saruhan-Direskeneli and Amr H. Sawalha 12 Common Genetic Component in Autoimmunity������������������������������������ 221 Gisela Orozco and Blanca Rueda Index�������������������������������������������������������������������������������������������������������������������� 237

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Systemic Lupus Erythematosus Susan K. Vester and Timothy J. Vyse

Contents 1.1  1.2  1.3  1.4 

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    1  are Monogenic Causes of SLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    2 R The MHC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    3 Polygenic Causes of SLE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    3 1.4.1  Common Variants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    5 1.4.2  Missing Heritability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    6 1.4.3  Rare Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    6 1.4.4  Structural Variants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    9 1.5  Epigenetic Mechanisms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    9 1.6  Modified Penetrance Through Regulatory Haplotypes. . . . . . . . . . . . . . . . . . . . . . . . . .  10 1.7  Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  11 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  11

1.1

Introduction

Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease. It is clinically very heterogeneous, and multiple organ systems can be affected including the skin, kidneys, nervous system, heart, lungs, and the musculoskeletal system, with severity of disease ranging from mild to life-threatening. SLE is characterized by the production of autoantibodies against cell nuclear components such as DNA, histones, and ribonucleoproteins [1]. The prevalence of S. K. Vester Department of Medical and Molecular Genetics, King’s College London, London, UK e-mail: [email protected] T. J. Vyse (*) Department of Medical and Molecular Genetics, King’s College London, Guy’s Hospital, London, UK e-mail: [email protected] © Springer Nature Switzerland AG 2019 J. Martín, F. D. Carmona (eds.), Genetics of Rare Autoimmune Diseases, Rare Diseases of the Immune System, https://doi.org/10.1007/978-3-030-03934-9_1

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SLE is ancestry-­dependent, being more prevalent in non-European populations such as Afro-­Caribbean, African American, and Asian. Ninety percent of patients are female, and in a majority of cases, onset of disease occurs during or after childbearing years [2, 3]. Juvenile onset makes up 15–20% of lupus cases, is less biased toward females, and is characterized with a more severe outcome and higher disease activity [4]. SLE is typically a genetically complex disease that has a strong genetic component, with an estimated heritability of 44–66% [5, 6]. Studies have shown that twin concordance rates are much higher in monozygotic twins (24–57%) than dizygotic twins (2%) [7, 8]. While siblings, parents, and offspring of affected patients have a higher than average risk of developing SLE (sibling risk λs of over 20), heredity is complex and in most cases does not follow a Mendelian pattern of inheritance [5, 9]. Earlier onset of disease and severe disease presentation are thought to correlate with higher genetic burden [10]. Environmental factors such as smoking; exposure to UV light; medication; viral infections, for instance, EpsteinBarr virus; and hormonal factors (estrogen, prolactin) contribute to the development of SLE, making this a multifactorial disease [11]. Pathways that have been implicated in the pathogenesis of SLE through genetic advances include impaired apoptosis and autophagy leading to clearance defects and thus increased exposure to nuclear autoantigens, type I interferon signaling including nucleic acid sensing and interferon (IFN) response, B lymphocyte hyperactivity, and altered immune cell signaling. Gaining a better understanding of the underlying pathways resulting in the development of SLE through genetic studies will lead the way in the development of targeted therapies. The clinically heterogeneous spectrum of SLE may represent several distinct, but related diseases that may respond differently to treatment [12].

1.2

Rare Monogenic Causes of SLE

While SLE is in most cases a complex genetic disease with polygenic inheritance, familial forms of lupus have been described as early as the 1950s [13–16]. Monogenic causes of lupus are rare, especially in adult cohorts, and disease phenotype can vary from classic manifestation. However, they provide clear genotype-­ phenotype relationships that give insight into the underlying biological pathways involved. Suggestive of a monogenic cause of lupus or lupus-like disease can be early age of onset; severe, atypical, or refractory manifestation; a family history of the disease (with Mendelian inheritance); parental consanguinity; or male gender [17]. Deficiencies in classical complement components were among the first to be described. Mutations have mainly been observed in early pathway components C1Q, C1R, C1S, C4, and C2, but some rarer are known in terminal pathway components C3 and C5–C9 [18]. While penetrance of these autosomal recessive complete complement deficiencies is high, not every individual will develop SLE as a result: 93% of C1Q deficiencies, 75% of C4 deficiencies, 57% of C1R/C1S deficiencies, 32–33% of C2 deficiencies, and 10% of C3 deficiencies develop SLE [19]. Of note,

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both C2 and C4’s C4A and C4B genes are clustered on the class III major histocompatibility complex (MHC) on chromosome 6 [20]. Complement deficiencies lead to an impaired uptake of apoptotic material, and both C1 and C4 deficiencies (which cause SLE in more than 75% of cases) are thought to additionally compromise self-­ tolerance [21]. While complement deficiencies have long been recognized as a cause of monogenic SLE, mutations in components of nucleic acid sensing, type I IFN, immune clearance, and lymphocyte signaling pathways have been discovered. This includes impaired clearance through an autosomal dominant inherited mutation in DNASE1 [22] or a DNASE1L3 null mutation, which leads to an autosomal recessive form of SLE characterized by pediatric onset of disease and kidney involvement [23]. A mutation in PRKCD (protein kinase Cδ) leading to dysregulated apoptosis and B cell proliferation was further found in a family with an autosomal recessive form of SLE [24]. Lupus-like diseases such as autosomal dominant familial chilblain lupus and autosomal recessive Aicardi-Goutières syndrome have shed further light on monogenic causal variants including TREX1, SAMHD1, RNASEH2A, RNASEH2B, RNASEH2C, ADAR, and IFIH1 involved in the nucleic acid sensing and the type I IFN pathway [25]. Other monogenic causes of lupus-like diseases have implicated over 16 further genes [26, 27].

1.3

The MHC

HLA antigens are extremely polymorphic due to selective pressure to present a wide variety of antigens. Associations between the MHC and SLE were first described in humans as early as 1971 [28, 29]. Classical MHC class II alleles HLA-DR3 (DRB1*0301) and HLA-DR2 (DRB1*1501), along with their haplotypes, have been consistently associated with SLE in European populations [30]. Indeed, in female-dominated European cohorts, the MHC region accounts for the greatest genetic risk of SLE [31–33]. Assessing causality of variants within the MHC is challenging due to the high linkage disequilibrium observed; however, it has become apparent that the MHC harbors multiple, distinct association signals [34–37].

1.4

Polygenic Causes of SLE

Up until the early 2000s, associations with the MHC, antibody receptor genes (e.g., FCGR2A), as well as monogenic complement deficiencies were some of the few known genetic factors of SLE [38]. Linkage analysis and candidate gene studies in subsequent years yielded further associations, including nine independently identified and replicated linkages. Meta-analysis of candidate genes confirmed association with genes such as PTPN22, IL10, CTLA4, and MBL [39]. However, it was not until the advent of new technologies such as genome-wide association studies (GWAS) and next-generation sequencing (NGS) that our knowledge of the genetics

2009

8.7% of SLE heritability explained

2011 2014

2015

2016

2017

28% of SLE heritability explained

2018

Identification of de novo variants in family trios

Large-scale transancestral Immunochip study

First SLE GWAS in Amerindian ancestry

Largest GWAS in Europeans to date finds overrepresentation of transcription factors

Resequencing of risk locus ITGAM identifies rare variants

Fig. 1.1  Timeline of major advances in the discovery of common and rare variants in SLE genetics. Shaded boxes indicate discovery of rare variants. See text for details

FCGR2A p.H131R variant associated with lupus nephritis

2008

First SLE GWAS in Asian ancestry

First SLE GWAS in European ancestry

1971

First reports of familial complement deficiencies

2007

HLA antigens are associated with SLE

1995

Rare TREX1 variants identified in SLE

Meta-analysis of European and Asian populations implicates genetic basis of ancestry-dependent prevalence of SLE

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of genetically complex diseases has enormously increased (see Fig. 1.1). A polygenic disease such as SLE will harbor contributions from both common and rare variants. Such sequence variation can take the form of single nucleotide polymorphisms (SNPs), approximately 90% of sequence variants observed, or structural variation, for instance, inversions or insertions and deletions, termed copy number variations (CNVs) [40, 41]. Common variants have a minor allele frequency (MAF) of 5% and over, while low-frequency variants have a MAF of 1–5%, and rare variants have a frequency of less than 1%. In the following, no distinction will be made between rare and low-frequency variants.

1.4.1 Common Variants Some years ago, the “common disease, common variant” hypothesis was put forward, suggesting that common variants with modest effect size are found throughout the population and will contribute to complex disease susceptibility [42, 43]. GWAS have been extensively utilized to identify association of common variants with disease. Studying this association is hampered by the fact that common variants are found in both healthy controls and in patients, albeit to a lesser extent in the control population if a variant is associated with disease risk. Common variants have been found to confer only modest risk with 1.1–1.5-fold odds ratios [44]. Both the frequency and effect size of a variant have an impact on the sample size needed to detect significant associations [45]. The first four GWAS in SLE in European populations were published 10 years ago in 2008, after many successful discoveries had been made in other autoimmune diseases [31–33, 46]. Nine of these loci were independently replicated the following year [47]. Since then a further three GWAS have been conducted in European populations [48–50], seven GWAS in Asian populations [51–57], and one GWAS in an Amerindian population [58]. Meta-analysis and large-scale replication studies soon followed [36, 59–62]. Over half of GWAS SNPs have been found to be shared between populations. The genetic risk score of SLE is lowest in European populations and increases in Amerindian and South Asian populations, then East Asian populations, and is highest in African populations [61]. This corresponds with prevalence of SLE in different ethnic groups. In addition, a large transancestral Immunochip study including European, African, and Hispanic Amerindian ancestry identified novel loci and showed that both ancestry-­ dependent and ancestry-independent SLE risk alleles exist [63]. The Immunochip includes SNPs and small insertion deletions for autoimmune and inflammatory diseases [64]. While in 2011 only 8.7% of SLE disease heritability could be explained [65], over 80 risk loci are now robustly associated with SLE risk at a genome-wide significance level of p 80 known SLE-associated genes. Some of these de novo mutations may be random background variation, which is why it is important to further study the variants and genes implicated. Five of the discovered variants, all CpG transitions, were observed at very low frequencies in ExAC, and five variants were predicted to be possibly damaging (CADD Phred score > 5). For one of these genes, C1QTNF4, found to be harboring a p.His198Gln de novo mutation, gene-­level metrics support a potential role in SLE, although no further rare variant associations were discovered for this gene. C1QTNF4 is constrained against missense variants and has a modest CADD score of 12.3. The function of C1QTNF4 is currently poorly understood; however, it has structural homology with both C1q and tumor necrosis factor (TNF) superfamily members, important pathways in the pathogenesis of SLE.  Mutant p.His198Gln-C1QTNF4 appears to inhibit some TNF-mediated cellular responses in  vitro, including NF-κB activation and TNF-­ induced cell death, suggesting a causal role of this mutation [94]. Another gene discovered to harbor a de novo mutation, PRKCB, has previously been reported in a consanguineous family with monogenic SLE [24] (see Sect. 1.2). While different pathogenicity-predictive tools for protein-coding variation are available [95], examining the role of rare, noncoding variants in WGS data is challenging. It is difficult to predict and test the functional significance of noncoding mutations, many of which will have no impact on pathogenesis.

1.4.3.3 Rare Variant Association Analysis Statistical methods to study association of rare variation comprise genomic region-­ based tests. These include burden tests, variance-component tests, combined tests, and others. Burden tests collapse causal genetic variants of the same effect size and direction into a single score, e.g., by gene, exon, or family of genes, and a regression analysis with the disease can then be performed [76]. Burden of rare variants might implicate genes in SLE pathogenesis that have so far not been identified as contributing to risk. Pullabhatla et al. [94] performed a burden test of rare exonic variants on imputed data and found association of rare variants in PRKCD with SLE. Furthermore, they observed association of collective rare exonic variants in the DNA methyltransferase DNMT3A with anti-dsDNA and renal involvement with hypocomplementemia in a subphenotype analysis. Variance-component tests examine the distribution of genetic effects within a set of variants, independently of the direction of the effect, which can be weighted [76]. Wang et al. [87] performed a sequence kernel association test to examine association of SLE with rare variants,

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discovered by resequencing of risk loci IKBKE and IFIH1. The publication was not able to show any significant association with these rare variants; however, they could be pathogenic in the individual. These association tests have limitations such as power, replication, and confounding effects, and using family-based designs can be beneficial [76].

1.4.4 Structural Variants Although structural variants can be both common and rare, GWAS prioritize SNPs which make up most of the sequence variation observed. Structural variants such as inversions or CNVs will also contribute to lupus susceptibility. CNVs often span whole or multiple genes and are likely to have a functional impact [73, 96]. While it is not easy to detect CNVs, new analysis tools for NGS data are making studying this structural variation easier. In particular interesting for a female-biased disease such as SLE, a disease-unrelated autosomal burden of rare CNVs has been reported in females [97]. Low copy number of the IgG Fc receptor FCGR3B gene is associated with SLE susceptibility [98–100]. Additionally, it has been suggested that complement C4 with CNV is a risk factor for SLE; however, extensive LD at the MHC makes this contentious [101].

1.5

Epigenetic Mechanisms

Epigenetic alterations such as DNA methylation, histone modification, DNA hydroxymethylation, or noncoding RNA are known to contribute to the development of SLE.  For instance, DNA hypomethylation and thus overexpression of inflammatory genes have been observed in SLE [102, 103]. Furthermore, DNA hypomethylation of apoptotic DNA appears to be involved in triggering autoantibody production [104]. Interestingly, rare variants in the DNA methyltransferase DNMT3A have been associated with SLE subphenotypes (see Sect. 1.4.3.2). SLE is predominantly observed in females, suggesting a direct or indirect effect of the sex chromosomes. Further insight into the role of the X chromosome in SLE has been gained from patients with X chromosomal abnormalities. The prevalence of patients with Klinefelter syndrome (47,XXY karyotype) in SLE is much higher than in the general population, with the risk of Klinefelter patients developing SLE much closer to female (46,XX), than male (46,XY) risk [105]. A higher prevalence of SLE was also reported in 47,XXX women than 46,XX women [106], and in addition, Turner’s syndrome (45,XO) may be underrepresented in SLE [107]. Together these findings suggest an X chromosome gene dosage effect [105–107], for which genes that escape X inactivation are potential candidates. Six loci harboring common variants associated with SLE have been identified on the X chromosome, including PRPS2, TLR7, CXorf21, LINC01420, NAA10-HCFC1-TMEM187, and IRAK1-­ MECP2, in different populations [48, 108–111].

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1.6

Modified Penetrance Through Regulatory Haplotypes

Both common regulatory and rare protein-coding variants, whether sequence variation such as SNPs or structural variation, make up the inter-individual variation that can lead to the development of disease. While variation is often studied on an individual variant level, variation should be viewed in context and not as independent entities, as there will, naturally, be interaction between a variant and its allelic setting. Coding variants especially, both in monogenic and polygenic inheritance, show a strong genotype-phenotype correlation; however, penetrance is often incomplete. While HLA variation has been viewed in terms of haplotypes for many years, it makes sense to think of other variation in the context of their regulatory haplotype. Noncoding variation has the ability to influence the expression of proteins, while pathogenic coding variation will have an impact on a gene’s function. Thus, cis-regulatory variation will have an impact on the expression of the pathogenic gene product and hence will contribute to the penetrance of directly disease-causing variants through dosage effects (see Fig. 1.2). Indeed, there appears to be a negative selection of haplotypes that leads to higher penetrance of pathogenic coding variants through expression or splicing [112]. In a disease such as SLE with high clinical variability, the interplay between regulatory and coding variation may well contribute to the clinical heterogeneity observed. Thinking about the complete genetic makeup makes sense, especially when considering population differences based on ethnicity and thus underlying genetic differences. An example of modified penetrance by the interplay of rare and coding variation has been reported in monogenic SLE. Demirkaya et al. [113] described a novel, high-penetrant mutation in C1R as a cause of familial SLE in a consanguineous Turkish family with four affected patients homozygous for a 1 base pair deletion. One of the more severely affected patients had more additional SLE risk alleles than the other three affected family members (no classic SLE-associated HLA alleles were identified in this family), while one young family member with the deletion was not yet affected. Other rare monogenic complement deficiencies leading to SLE (see Sect. 1.2) have high,

silencer

enhancer

regulatory variant

promoter

regulatory variant regulatory haplotype

5’UTR

exon

intron

protein-coding variant

3’UTR

regulatory variant

amount of pathogenic protein higher penetrance lower penetrance

Fig. 1.2  Modified penetrance of protein-coding variation through regulatory haplotypes. Penetrance of protein-coding variation is dependent on the allelic regulatory haplotype made up of common or rare noncoding variants. The amount of mutated, here pathogenic, protein produced is dependent on how regulatory variation influences expressivity of this protein. UTR untranslated region

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but incomplete penetrance. This implicates a role of modifying alleles in disease expressivity, as rare and common gene variants will collectively contribute to disease severity. Non-HLA haplotypes that influence risk of developing SLE have been described. IRF5 haplotypes, consisting of a splice site variant, an exonic variant, and a variant in the 3′ untranslated region (3′UTR), are either protective or confer risk, demonstrating that multiple, in this case common, variants can jointly influence disease risk [86]. Furthermore, a TREX1 risk haplotype made up of common variants was relatively common in European ancestry and had a higher frequency of neurological involvement [82]. Thus, both risk of developing SLE and severity of disease manifestation may be influenced by rare and common variant haplotypes.

1.7

Conclusions

In the last decade, much progress has been made in identifying the underlying genetic causes of SLE. Both common and rare variants have been identified; however, missing heritability is still observed. Initially, high-penetrant rare monogenic forms of SLE were described in family-based studies. Common variants have mainly been identified through population-based studies, especially since the advent of GWAS in 2008. However, identifying functionally causative variants from GWAS hits is not trivial, and thus the underlying pathogenic pathways remain poorly understood. While the interplay between variants remains understudied, observing them in terms of their haplotype is going to find increasing application. Elucidating the causal impact of risk variants and their underlying pathogenic pathways is of pivotal importance for exploring new therapeutic approaches. Family-based studies, especially of extreme phenotypes, yielding high-penetrant rare variants can inform new core genes and pathways. These are of particular importance for improving care of SLE patients, as these rare variants have the potential to be targeted therapeutically.

References 1. Kaul A, Gordon C, Crow MK, Touma Z, Urowitz MB, van Vollenhoven R, et al. Systemic lupus erythematosus. Nat Rev Dis Primers. 2016;2:16039. 2. Pons-Estel GJ, Alarcón GS, Scofield L, Reinlib L, Cooper GS.  Understanding the epidemiology and progression of systemic lupus erythematosus. Semin Arthritis Rheum. 2010;39(4):257–68. 3. Rees F, Doherty M, Grainge MJ, Lanyon P, Zhang W. The worldwide incidence and prevalence of systemic lupus erythematosus: a systematic review of epidemiological studies. Rheumatology. 2017;56(11):1945–61. 4. Brunner HI, Gladman DD, Ibanez D, Urowitz MD, Silverman ED.  Difference in disease features between childhood-onset and adult-onset systemic lupus erythematosus. Arthritis Rheum. 2008;58(2):556–62. 5. Kuo C-F, Grainge MJ, Valdes AM, See L-C, Luo S-F, Yu K-H, et al. Familial aggregation of systemic lupus erythematosus and coaggregation of autoimmune diseases in affected families. JAMA Intern Med. 2015;175(9):1518–26.

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2

Systemic Sclerosis Elena López-Isac, Marialbert Acosta-Herrera, and Javier Martín

Contents 2.1  I ntroduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    2.2  Genetic Component. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    2.2.1  Systemic Sclerosis Heritability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    2.2.2  The HLA Class II in Systemic Sclerosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    2.2.3  Overview of the Genetic Component of Systemic Sclerosis Outside the HLA Region: Functional Implication of Associated Loci. . . . . . . . . . . . . . .   2.3  Conclusions and Future Directions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   

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Introduction

Systemic sclerosis or scleroderma (SSc) is a rare and complex autoimmune disease (AD) of the connective tissue with heterogeneous clinical manifestations. It involves deregulation of the immune system, vascular damage, and extensive collagen deposition leading to fibrosis in the skin and different internal organs [1–3]. The main vascular abnormalities include Raynaud’s phenomenon, renal crisis, and pulmonary arterial hypertension (PAH). The lungs, heart, kidneys, and esophagus are the principal internal organs affected by fibrosis, although this complex disease can cause severe dysfunction and failure of almost any internal organ. Esophageal dysfunction is the most common visceral complication; however lung involvement (both pulmonary hypertension and pulmonary fibrosis) is the leading cause of

E. López-Isac (*) · M. Acosta-Herrera · J. Martín Institute of Parasitology and Biomedicine López-Neyra (IPBLN), CSIC, PTS Granada, Granada, Spain e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2019 J. Martín, F. D. Carmona (eds.), Genetics of Rare Autoimmune Diseases, Rare Diseases of the Immune System, https://doi.org/10.1007/978-3-030-03934-9_2

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death [4]. Immune imbalance includes altered lymphocyte activation that leads to autoantibody production, aberrant cytokine release, and deregulation of the innate immune system. The most frequent autoantibodies are anticentromere (ACA), antitopoisomerase (ATA), and anti-RNA polymerase III autoantibodies (ARA) [1, 2]. Patients with SSc are usually classified into two main subgroups according to the extent of skin involvement, limited cutaneous SSc (lcSSc) and diffuse cutaneous SSc (dcSSc), with a prevalence of approximately 65% and 35%, respectively. In lcSSc, fibrosis is mainly restricted to the skin of the hands, arms, and face. Raynaud’s phenomenon appears several years before fibrosis, and pulmonary arterial hypertension (PAH) is frequent. dcSSc is characterized by a more aggressive, generalized, and rapidly progressing fibrosis that affects the skin of all body and one or more visceral organs [1–3]. The occurrence of this disease is more frequent among females, with a woman-­ to-­man ratio ranging from 3:1 to 12:1 [5]. However, a recent study has reported a correlation between male sex and a worse outcome of the disease [6]. Large discrepancies exist regarding SSc prevalence, ranging from 7 to 700 cases per million inhabitants. These differences may be the result of substantial variation across geographic regions, ethnic differences, and the lack of consensus on disease diagnosis and classification due, mainly, to the disease rarity and to the large spectrum of clinical manifestations and severity [6, 7]. The estimated prevalence is higher among African descent population than in European descent, and the lowest prevalence has been reported in Asian population [7, 8]. Furthermore, it has been observed an increasing north to south gradient in SSc incidence across Europe. Interestingly, the presence of autoantibodies is also differential across ethnicities being the ACA more frequent in European descent population and the anti-U1-ribonucleoprotein (RNP) and anti-U3-RNP (fibrillarin) in African descent population [9]. Despite the progress in understanding the pathophysiology of SSc, the pathogenic mechanisms underlying the disease are still far from its complete knowledge. The conventional proposed model suggests that microvascular injury and endothelial cell (EC) activation are the primary events in SSc [10–12]. The hypothesis arises from the observation that vascular damage (Raynaud’s phenomenon and edema) is the earliest feature that takes place in the disease. However, recent studies have strengthened the idea of autoimmunity as a central player in the onset and progression of the disease [13, 14]. The vascular inflammatory phase is more prominent in the earlier stages of SSc. This inflammatory environment and the altered immune reaction ultimately give rise to fibroblast activation, excessive deposition of collagen and other components of the extracellular matrix (ECM), and fibrosis [11]. In addition, several studies have also implicated reactive oxygen species (ROS) in the pathogenesis of SSc [15]. Tissue ischemia and activated fibroblast can lead to the generation of these chemical species. In fact, high levels of ROS have been observed in SSc, and several in vitro and mouse model studies further support the profibrogenic effect of these chemical species in fibroblast [15–17]. SSc shows a complex etiology where environmental and genetic factors seem to play a major role in the disease [2]. Several environmental factors have been linked to SSc susceptibility, such as the exposure to chemical compounds (e.g., silica,

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organic solvents, welding fumes, or asbestos), infectious agents (e.g., parvovirus B19, cytomegalovirus, and Epstein-Barr virus), and pregnancy-related events [2]. The current knowledge of the SSc genetic component will be addressed in the following sections of the chapter.

2.2

Genetic Component

The most compelling evidence supporting the existence of a genetic component in SSc comes from family co-occurrence. In fact, a positive familial history of SSc is the major risk factor reported to date [18]. A study comprising 703 families, first-­ degree relatives and siblings of patients with SSc, showed relative risk ranging from 10- to 27-fold higher than in the general population [18]. Moreover, familial and twin studies have described a high concordance of autoantibody production [19, 20]. In addition, there is interesting evidence pointing out to this genetic component, given the aforementioned differential prevalence among different populations. The starting point for understanding the genetic bases of complex diseases is the identification of genetic markers associated with them. Single nucleotide polymorphisms (SNPs) are variations at a single position in the DNA sequence. The association of these variants with a phenotype is determined by genetic association studies, and the most commonly used are bi-allelic SNPs. Case-control studies compare the allele frequencies of one or more SNPs between cases (individuals affected by the disease) and controls (unaffected individuals). If the difference between these frequencies is statistically significant, that is, the p-value for association is below the significance threshold, then the SNP is considered as associated with the disease [21]. Nowadays, and thanks to advances in the genotyping technology and large sequencing projects, the assessment of genetic associations is directed to the entire genome in genome-wide association studies (GWASs). They have the main advantage of exploring the genome without the need of having a previous hypothesis about the biological processes involved. Besides, they are hypothesis generating, since the novel discovered loci may pinpoint to unexpected pathways in the pathogenesis of the disease. With the help of public information from the HapMap project [22] and The 1000 Genomes project [23] through statistical inferences, the determination of millions of SNPs provides an excellent power to infer common genetic variation in European population. Despite the success provided by GWAS, the majority of associated SNPs have modest effect sizes, and they explain a relatively small proportion of the heritability of many complex diseases [24]. Therefore, it has been proposed that part of this “missing heritability” may lie on non-well covered region from arrays or on rare variants with large effect sizes. This issue has led to the design of a genotyping array called Immunochip, with the aim to perform a fine-mapping of loci associated with immune-mediated diseases in a cost-effective manner [25]. This custom genotyping platform contains 196,524 variants (including SNPs and small insertions-deletions) for fine-mapping 186 autoimmunity loci and a dense coverage of the HLA region.

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The study of the genetic basis underlying SSc started with candidate gene studies, using relatively small sample sizes. In spite of this, some of them were able to identify susceptibility genes that are currently considered as firmly associated genes with the disease, such as STAT4, IRF5, and the HLA region [26]. In 2010, the first GWAS in SSc in European population was published [27]. Our group was involved in the study, which identified CD247 as a novel gene associated with SSc risk, and confirmed the previously reported associations in the HLA region, STAT4 and IRF5. Interestingly, these findings were independently replicated by Dieudé et al. [28]. One year later, a second GWAS in SSc was published by Allanore et al., which identified the genes TNIP1, RHOB, and PSORS1C1 as novel susceptibility loci [29]. Our group was involved in the independent replication of the findings from this second GWAS; however, we could only confirm TNIP1 signal as a genetic risk factor for SSc [30]. This fact highlights the relevance of a high statistical power in GWAS, since GWAS results tend to present inflated effect sizes—also called the winner’s curse. Additionally, two other SSc GWAS have been performed in Korean population and a trans-ethnic metaGWAS involving European and Japanese patients, respectively [31, 32]. Another distinctive trait of GWASs is the so-called gray zone, where SNPs with tier 2 associations (p-values between 5 × 10−8 and 5 × 10−3) are located. Follow-up studies focusing on this gray zone constitute one of the most useful GWAS data mining methods, since possible real association signals could be masked in that area due to a lack of statistical power. In SSc, these types of studies have been successful in the identification of new risk loci. Bossini-Castillo et al. performed a follow-up focused on IL12RB2, a locus that showed suggestive signal of association in the first SSc GWAS [33]. Additionally, Martin et  al. carried out a large follow-up of the GWAS that included 768 polymorphisms selected from the gray zone, and they could identify CSK as a genetic risk factor for SSc and confirmed previously reported associations [34]. Taking advantage of our GWAS data, we also performed a follow-up of the gray zone from the French GWAS [35] confirming PPARG as a susceptibility locus. The Immunochip has gathered important achievement on the genetic component of ADs [36]. Applying this fine-mapping approach, our group identified several new SSc susceptibility loci (DNASE1IL3, IL12A, and ATG5) that implicated new biological pathways into the pathogenesis of the disease, such as apoptosis and autophagy [37]. In addition, an extensive analysis of the HLA region was performed. Later on, a second SSc Immunochip performed in an Australian cohort with relatively small sample size confirmed some of the reported associations [38].

2.2.1 Systemic Sclerosis Heritability The large numbers of SNPs provided by GWAS and Immunochip have offered great opportunities to develop new methodologies for predicting genetic risk of complex diseases in a more accurate way [39–41]. In the case of SSc, efforts in the estimation of disease heritability have not provided conclusive reports, mainly due to limited sample sizes [26]. The significant SNPs described to date for SSc only account for

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~20% of the estimated heritability [26]. Thus, it is expected that additional SSc risk loci remain to be discovered. Moreover, the number of well-established susceptibility loci is relatively low in comparison with other ADs, such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), for which 101 and 43 loci have been validated, respectively [42]. The low prevalence of SSc makes difficult the recruitment of large sample sizes required to reach a high statistical power and to effectively detect association signals. Therefore, the estimation of the heritability of SSc is a challenging task.

2.2.2 The HLA Class II in Systemic Sclerosis The major histocompatibility complex (MHC) is the genetic region that represents the strongest genetic association reported to date for SSc [37], as well as for other ADs [36]. This implies that disease-associated MHC alleles must be responsible for an important portion of the susceptibility to autoimmune processes. In human, the MHC is the most polymorphic region of the genome, and its gene products are called human leukocyte antigen (HLA). Several HLA associations with SSc have been previously described, and all of them were found in HLA class II genes [36].The analyses according to the autoantibody status showed differential associations in the HLA genes [37, 43, 44]. Moreover ancestry has been proven to influence the association of classical HLA alleles associated with SSc [43, 45].This was clearly observed in the study by Arnett et al., which included 961 European descent, 178 African descent, and 161 Hispanic descent SSc patients and 1000 healthy controls (539 Caucasian, 263 African-­American, and 198 Hispanic subjects) [43]. For the overall disease, the strongest associations were found for the DRB1*1104, DQA1*0501, and DQB1*0301 haplotype and DQB1 alleles encoding a non-leucine residue at position 26 (DQB1 26 epi) in the white and Hispanic subjects, while DRB1*0804, DQA1*0501, and DQB1*0301 alleles showed the strongest signals for the black cohort. Regarding ATA-positive subgroup, HLADPB1*1301 was the allele most strongly associated in European descent subjects (OR = 14), in accordance with previous results [46]. However, HLA-DRB1*1104 was the allele that showed the most significant association and the highest OR for this autoantibody in the Hispanic populations, while HLA-DRB1*1104, HLADQB1 26 epitope, HLA-DRB1*08, and HLA-­DRB1*0804 alleles were those that explained the association with ATA in the African descent. ACA-positive status was best explained by DQB1*0501 and DQB1*26 epi alleles in white and Hispanic subjects. The association analysis in ARA-positive patients revealed significant results for HLA-DRB1*0404, HLA-­ DRB1*11, and HLA-DQB1*03  in European and Hispanic populations but for DRB1*08 in African descent population [43]. The most comprehensive analysis of the HLA alleles in SSc reported to date was carried out in the first SSc Immunochip study published by Mayes et al. [37]. As stated above, this custom genotyping array includes a dense coverage of this genomic region, along with novel imputation methods that enable the inference of classical HLA alleles, and even polymorphic amino acid positions from genetic data

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allowed the authors to describe a comprehensive model that explained all the observed associations in the region in European descent population. The model includes six polymorphic amino acid positions in HLA-DRB1, HLA-DQA1, and HLA-DPB1 and seven SNPs independently associated. The analysis also confirmed the divergent HLA allele associations between ACA-positive and ATA-positive serological subgroups [37]. In the ACA-positive subgroup, the model includes 2 amino acid associations, the 13th HLA-DRB1 amino acid and the 69th HLA-DQA1 amino acid, along with 5 single-nucleotide polymorphisms (SNPs). The model provided for ATA-positive subgroup comprises 4 amino acid associations, the 67th and 86th positions in HLA-DRB1 and the positions 76th and 96th in HLA-DPB1, along with 2 SNPs [37]. Mayes et al. also reported that some of the associated protein residues cause different amino acid binding preferences in the corresponding peptide binding grooves of the HLA molecules. Moreover, the authors functionally characterized the associated SNPs and found cis-expression quantitative trait loci (eQTLs) for most of them [37].

2.2.3 O  verview of the Genetic Component of Systemic Sclerosis Outside the HLA Region: Functional Implication of Associated Loci In this section we would like to revisit and update the firmest genetic associations with SSc and their potential functional role in the disease pathophysiology. The genetic variants associated with the disease are mainly located in genes related to the immune response, either in the innate or in the adaptive immune system. However, well-powered high-throughput studies have contributed to highlight novel loci with different roles on the disease presentation. In total, there are 20 loci outside the HLA region firmly associated to SSc (Table 2.1). Most of them point out to genes that are closely related according to their function and that form complex interaction networks, as can be observed in a protein-protein interaction (PPI) analysis performed by means of STRING V10.0 (Fig. 2.1) [47]. These molecular networks point toward biological pathways involved in SSc onset and progression.

2.2.3.1 Innate Immunity, Interferon Signature, and Systemic Sclerosis As it was stated in the introduction section, the inflammatory phase is an important feature of SSc pathophysiology [2, 11, 48–50]; therefore, it is not surprising that several loci directly involved in the regulation of the inflammatory response represent SSc susceptibility genes [29, 30, 34, 37, 51] (Table 2.1). This is the case of the genes TNFAIP3 and TNIP1, which are involved in the TNF-induced NF-κB proinflammatory signaling pathway and participate in the TNF-mediated apoptosis [52]. Type I interferon (IFN) signaling, which is a master regulator of the innate immune system, stimulates natural killer and cytotoxic T-cell activity and the maturation of antigen-presenting cells (APCs). The deregulation of this pathway has

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Table 2.1  Firm susceptibility loci for systemic sclerosis outside the HLA region Chr Gene name Index SNP Locus Innate immunity, interferon signature, and inflammation IRF5 7 IFN regulatory factor 5 rs36073657

Intronic

Reference [27, 37, 53–58] [44, 59] [60] [61] [32] [34, 37, 51]

IFN regulatory factor 8 rs11117420 Intergenic IFN regulatory factor 7 rs1131665 Exonic IFN regulatory factor 4 rs9328192 Intergenic PR/SET domain 1 rs4134466 Intergenic Tumor necrosis factor rs2230926 Exonic alpha-induced protein TNIP1 5 TNFAIP3-interacting rs3792783 Intronic [29, 30] protein Adaptive immune response: B- and T-cell proliferation, survival, and cytokine production TNFSF4 1 Tumor necrosis factor ligand rs11576547 ncRNA_ [69–71] superfamily member 4 intronic CD247 1 T-cell receptor zeta chain rs2056626 Intronic [27–29] CSK 15 C-Src rs1378942 Intronic [34] STAT4 2 Signal transducer and rs3821236 Intronic [27, 29] activator of transcription 4 TYK2 19 Tyrosine kinase 2 rs34536443 Exonic [80] IL12A 3 Interleukin 12A rs589446, ncRNA_ [37] rs77583790 intronic Intronic [33] IL12RB2 1 Interleukin 12 receptor, beta rs3790566 2 UTR5 [79] IL12RB1 19 Interleukin 12 receptor, beta rs436857 1 BLK 8 BLK proto-oncogene, Src rs2736340 Intergenic [74–76] family tyrosine kinase Apoptosis, autophagy, fibrosis, and others DNASE1L3 3 Deoxyribonuclease I-like 3 rs35677470 Exonic [37, 38] ATG5 6 Autophagy related 5 rs9373839 Intronic [37] PPARG 3 Peroxisome proliferator-­ rs310746 Intergenic [35, 81] activated receptor gamma GSDMA 17 Gasdermin A rs3894194 Exonic [32]

IRF8 IRF7 IRF4 PRDM1 TNFAIP3

16 11 6 6 6

SNP function

Chr chromosome, SNP single-nucleotide polymorphism

been observed in the pathogenesis of SSc, where an increased expression and activation of IFN-inducible genes have been detected in the peripheral blood and skin of patients [53]. This evidence, known as “IFN signature,” is similar to the one observed in other ADs. It is noteworthy that four IFN regulatory factor (IRF) genes have been associated with SSc susceptibility: IRF4, IRF5, IRF7, and IRF8 [27, 37, 44, 53–61]. These IRFs belong to a family of transcription factors that are activated after type I IFN induction [53, 62]. In the first GWAS in Caucasian population performed by Radstake et al. [27], the lead association outside the HLA region was in a promoter SNP of IRF5. Moreover, this SNP is associated with lower transcriptional activity of the gene, a longer survival, and less severity of interstitial lung

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E. López-Isac et al. CD247 PPARG CSK BLK GSDMA

HLA-DQA1 TNFAIP3

HLA-DRB1 HLA-DPB1

TNFSF4

DNASE1L3

IRF7 TNIP1 IRF4 IRF5

IRF8

STAT4

IL12A

TYK2

IL12RB2

PRDM1 IL12RB1

ATG5

Fig. 2.1  Protein-protein interaction network of systemic sclerosis risk loci performed by means of STRING V10.0. The thickness of the lines depicts the confidence of data supporting the interaction. Proteins related to the immune system are highly connected. Additional associated loci highlight new biological processes also relevant in the disease pathogenesis

disease (ILD) in SSc patients [63]. Confirming the connection of SSc with the “IFN signature,” the gene PRDM1 has been recently associated with the disease in a trans-ethnic meta-GWAS [32]. The protein encoded by this gene acts as a repressor of the β-interferon gene expression.

2.2.3.2 Adaptive Immune Response: B- and T-Cell Proliferation, Survival, and Cytokine Production Several components of the adaptive immune response are also involved in the disease pathophysiology, and this is reflected, as well, in the genes that have been associated with the disease. T cells show an activated phenotype and signatures of antigen-driven cell expansion [64–66]. Specifically, most of the cells observed in SSc infiltrates are T helper 2 (Th2) cells, characterized by secretion of profibrotic mediators. Moreover, a Th1-Th2 cytokine imbalance with higher levels of Th2 cytokines has been described in patients with SSc [67]. B cells, on the other hand, show dysregulated homeostasis and are also detected in skin and lung infiltrate of patients

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with SSc [68]. Their role is not only restricted to autoantibodies secretion but also the production of IL-6, which directly stimulates fibroblasts. T- and B-cell biology, including cell proliferation, differentiation, survival, and activation, are represented in SSc risk factors [69–71]. For instance, TNFSF4 is involved in T- and B-cell proliferation and survival [72]; the gene CD247 encodes the T-cell receptor subunit that forms the T-cell receptor-CD3 complex (TCR-CD3 complex), which is negatively regulated by CSK and LYP [27, 73].The gene BLK, also associated with SSc genetic predisposition [74–76], is a tyrosine kinase involved in B-lymphocyte development, differentiation, and BCR signaling [77]. The gene PRDM1, also known as B-lymphocyte-induced maturation protein 1, is very important for epithelial and B-cell differentiation [78]. Additionally, IL12/23 and Jak/STAT signaling pathways are also overrepresented by the genetic background of SSc with associated genes such as IL12A, IL12RB1, IL12RB2, STAT4, and TYK2 [27, 33, 37, 79, 80] (Table 2.1).

2.2.3.3 Apoptosis, Autophagy, and Fibrosis Different loci in pathways belonging to the immune system are the main source of genetic risk factors in SSc. However, additional associated genes highlight new biological processes also relevant in the disease pathogenesis [32, 35, 37, 38, 81]. The gene DNASE1L3 is a member of the deoxyribonuclease I family and is involved in DNA fragmentation, DNA breakdown during apoptosis, and the generation of the resected double-strand DNA breaks in immunoglobulin genes [82–84]. Another gene recently associated with SSc susceptibility is the GSDMA [32]. GSDM family proteins have a role in the regulation of cellular differentiation, likely through programmed cell death [85]. Consequently, the impaired clearance of degraded DNA and apoptosis emerges as a new process related to the disease. Moreover, autophagy is a central player in the immune system. It is involved in B- and T-cell development, survival, cytokine production, and pathogen clearance [86]. In this sense, the gene product of ATG5, a gene associated with SSc [37], is involved in autophagosome elongation. Finally, excessive collagen deposition and fibrosis is one of the hallmarks of SSc. Therefore, it can be expected that genes related to this process are also associated with the disease [35]. This is the case of the locus PPARG, which encodes the peroxisome proliferator-activated receptor gamma. Recent studies have shown the antifibrotic role of this molecule in vitro and in vivo [87–90] and the impairment on its expression and function in SSc patients [91]. 2.2.3.4 Functional Characterization of Disease-Associated Variants In order to provide insights into the pathological mechanisms leading to a complex disease, it becomes necessary to assign the functional consequences of the statistical associated variants. The first challenge is to identify the causal variants that are responsible for the genetic associations, as not always correspond to the lead SNP. Once the variants are identified, the next step is to connect the genetic markers to likely target genes. The development of ambitious projects focused on improving the functional characterization of human genome and epigenome, including the

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ENCODE, the NIH Roadmap Epigenomics, and GTEx, has offered a valuable source of data for SNP functional annotation and prioritization [92–94]. In the case of SSc, several associated SNPs constitute missense variants, and their functional effect has been previously addressed. For instance, in vitro studies have shown that rs35677470 minor allele at DNASE1L3 leads to an inactive form of the protein that lacks its DNase activity [95]. Similarly, TYK2 rs34536443 minor allele leads to a near-complete loss of TYK2 catalytic function, and consequently it impairs cytokine signaling [96]. Another example is the non-synonymous SNP at TNFAIP3 that results in a phenylalanine-to-cysteine change that reduces the inhibitory activity of TNFAIP3 at the NF-κB signaling pathway [97]. However, the vast majority of associated SNPs lie in noncoding regions of the genome, as is the case for other complex traits [98]. These SNPs might be linked to regulatory functions, rather than affecting the encoded protein themselves. In this sense, the different alleles might correlate with changes on gene expression, also known as eQTL, or overlap with chromatin marks of active enhancers (H3K4me1, H3K27ac), active promoters (H3K4me3, H3K9ac) [99], DNase hypersensitivity sites, or TF-binding sites. Most of SSc-index SNPs overlap with promoter and enhancer histone marks in relevant cell types for the disease, such as primary T helper cells, primary B cells, primary CD8+ cells, monocytes, and fibroblasts. This fact confirms that most of the genetic variations involved in the susceptibility to SSc modulate transcriptional regulatory mechanisms. Additionally, many of the associated variants correlate with eQTLs, thus altering gene expression for the a priori candidate gene. Nonetheless, we also found genetic variants affecting additional genes. As an example, DNASE1L3 rs35677470 missense variant—that leads to an inactive enzyme—correlates with eQTL for the neighbor genes PXK and RP11-802O23.3. These observations highlight that assigning association signals to the “nearest gene” is not a suitable strategy for some SNPs and that the functional role of certain signals may spread out to different target genes. In addition to variants mapping in introns and intergenic regions, there are a number of SNPs located in noncoding RNAs (ncRNAs), in UTR3′ or UTR5′ regions, upstream or downstream the genes, and in splicing sites. This suggests that variants affecting mRNA processing or stability may provide additional insights into the regulatory mechanisms affecting expression of disease-implicated loci. Emerging evidence suggests a role of ncRNAs in autoimmunity [100], and there are also increasing evidence for the role of genetic variants underlying transcript splicing (splicing quantitative trait loci or sQTLs) in common diseases [101]. The establishment of the regulatory mechanisms underlying a genetic association is not always straightforward [102]. Interestingly, due to the LD patterns, the functional annotation of the index SNP might be linked to a different “SNP category.” For instance, the index SNP at IL12RB1 is a promoter variant, and its minor allele showed to decrease IL12RB1 mRNA levels [103]. This SNP is in high LD with other exonic non-synonymous, intronic, UTR3′, and splicing variants. For example, the variant rs393548 that leads to an alternative 3′ acceptor splice site results in a different transcript for the gene.

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The functional characterization on epigenetic marks and relevant cell types or tissues for the disease adds valuable information to accurately design functional studies that will help us to move from genetic associations to disease genes and mechanisms [104].

2.3

Conclusions and Future Directions

Genetic association studies have been successful in determining genetic regions containing variants associated with SSc risk; however, assigning them a functional effect and uncovering the disease-causing genes is still a challenging task [26]. Until now, GWAS findings are assigned to the closest or most compelling local gene; however, the majority of associated variants lie in noncoding regions of the genome and are known to be enriched in enhancer regions, which are cell-type specific [26, 93]. Enhancers can regulate gene transcription by physical interactions with their target genes, and these can operate over large genetic distances [105]. Therefore, to fully translate these findings into mechanisms of disease and potential therapeutic targets, one of the next fundamental steps is to confidently link the associated genomic regions to genes and cell types on which they have their disease-­ causing effect. Recent advances in molecular biology techniques, including nuclear dynamic technologies, have enabled the design of functional experiments to address these questions. Chromosome conformation capture technology (Capture Hi-C) has been successfully used to detect patterns of long-range interactions at the genome-wide level at high resolution between chromosomal regions in several types of cancer [106–108] and in four autoimmune diseases: RA, type 1 diabetes (T1D), psoriatic arthritis (PsA), and juvenile idiopathic arthritis (JIA) [109]. Therefore, Capture Hi-C (CHi-C) has been shown to be an effective approach to link noncoding variation to target genes, which is an important challenge for genetics and genomics of complex diseases such as SSc [108, 110].

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69. Bossini-Castillo L, Broen JC, Simeon CP, Beretta L, Vonk MC, Ortego-Centeno N, et al. A replication study confirms the association of TNFSF4 (OX40L) polymorphisms with systemic sclerosis in a large European cohort. Ann Rheum Dis. 2011;70(4):638–41. 70. Gourh P, Arnett FC, Tan FK, Assassi S, Divecha D, Paz G, et  al. Association of TNFSF4 (OX40L) polymorphisms with susceptibility to systemic sclerosis. Ann Rheum Dis. 2010;69(3):550–5. 71. Coustet B, Bouaziz M, Dieude P, Guedj M, Bossini-Castillo L, Agarwal S, et al. Independent replication and meta analysis of association studies establish TNFSF4 as a susceptibility gene preferentially associated with the subset of anticentromere-positive patients with systemic sclerosis. J Rheumatol. 2012;39(5):997–1003. 72. Gourh P, Tan FK, Assassi S, Ahn CW, McNearney TA, Fischbach M, et al. Association of the PTPN22 R620W polymorphism with anti-topoisomerase I- and anticentromere antibody-­ positive systemic sclerosis. Arthritis Rheum. 2006;54(12):3945–53. 73. Zhong MC, Veillette A.  Immunology: Csk keeps LYP on a leash. Nat Chem Biol. 2012;8(5):412–3. 74. Gourh P, Agarwal SK, Martin E, Divecha D, Rueda B, Bunting H, et al. Association of the C8orf13-BLK region with systemic sclerosis in North-American and European populations. J Autoimmun. 2010;34(2):155–62. 75. Ito I, Kawaguchi Y, Kawasaki A, Hasegawa M, Ohashi J, Kawamoto M, et al. Association of the FAM167A-BLK region with systemic sclerosis. Arthritis Rheum. 2010;62(3):890–5. 76. Coustet B, Dieude P, Guedj M, Bouaziz M, Avouac J, Ruiz B, et al. C8orf13-BLK is a genetic risk locus for systemic sclerosis and has additive effects with BANK1: results from a large French cohort and meta-analysis. Arthritis Rheum. 2011;63(7):2091–6. 77. Texido G, Su IH, Mecklenbrauker I, Saijo K, Malek SN, Desiderio S, et  al. The B-cell-­ specific Src-family kinase Blk is dispensable for B-cell development and activation. Mol Cell Biol. 2000;20(4):1227–33. 78. Ott G, Rosenwald A, Campo E. Understanding MYC-driven aggressive B-cell lymphomas: pathogenesis and classification. Blood. 2013;122(24):3884–91. 79. Lopez-Isac E, Bossini-Castillo L, Guerra SG, Denton C, Fonseca C, Assassi S, et  al. Identification of IL12RB1 as a novel systemic sclerosis susceptibility locus. Arthritis Rheumatol. 2014;66(12):3521–3. 80. Lopez-Isac E, Campillo-Davo D, Bossini-Castillo L, Guerra SG, Assassi S, Simeon CP, et al. Influence of TYK2 in systemic sclerosis susceptibility: a new locus in the IL-12 pathway. Ann Rheum Dis. 2016;75(8):1521–6. 81. Marangoni RG, Korman BD, Allanore Y, Dieude P, Armstrong LL, Rzhetskaya M, et  al. A candidate gene study reveals association between a variant of the Peroxisome Proliferator-­ ­ Activated Receptor Gamma (PPAR-gamma) gene and systemic sclerosis. Arthritis Res Ther. 2015;17:128. 82. Shiokawa D, Tanuma S. Characterization of human DNase I family endonucleases and activation of DNase gamma during apoptosis. Biochemistry. 2001;40(1):143–52. 83. Errami Y, Naura AS, Kim H, Ju J, Suzuki Y, El-Bahrawy AH, et al. Apoptotic DNA fragmentation may be a cooperative activity between caspase-activated deoxyribonuclease and the poly(ADP-ribose) polymerase-regulated DNAS1L3, an endoplasmic reticulum-­ localized endonuclease that translocates to the nucleus during apoptosis. J Biol Chem. 2013;288(5):3460–8. 84. Okamoto M, Okamoto N, Yashiro H, Shiokawa D, Sunaga S, Yoshimori A, et al. Involvement of DNase gamma in the resected double-strand DNA breaks in immunoglobulin genes. Biochem Biophys Res Commun. 2005;327(1):76–83. 85. Aglietti RA, Dueber EC. Recent insights into the molecular mechanisms underlying pyroptosis and gasdermin family functions. Trends Immunol. 2017;38(4):261–71. 86. Bhattacharya A, Eissa NT.  Autophagy and autoimmunity crosstalks. Front Immunol. 2013;4:88. 87. Burgess HA, Daugherty LE, Thatcher TH, Lakatos HF, Ray DM, Redonnet M, et  al. PPARgamma agonists inhibit TGF-beta induced pulmonary myofibroblast differentiation

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3

Behçet’s Disease Lourdes Ortiz-Fernández and Maria Francisca González-Escribano

Contents 3.1  I ntroduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    3.2  Genetic Component of Behçet’s Disease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    3.2.1  HLA Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    3.2.2  Non-HLA Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    3.3  Molecular Pathways. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    3.4  Conclusions and Future Perspectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   

3.1

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Introduction

Behçet’s disease (BD) is a complex systemic syndrome characterized by inflammatory lesions of blood vessels throughout the body, being small vessels the most frequently involved. This pathology is a rare and debilitating vasculitis, which presents a wide range of clinical phenotypes. The main clinical features are genital ulceration, ocular involvement (mainly uveitis), and skin lesions, but patients also can suffer gastrointestinal involvement, arthritis, and neurological disorders, among other symptoms, which lead to significant morbidity and mortality [1]. The lack of a pathognomonic sign and the absence of specific biomarkers of the disease make difficult the diagnosis of BD which is based on criteria and classification systems

L. Ortiz-Fernández (*) Instituto de Parasitología y Biomedicina ‘López-Neyra’, IPBLN-CSIC, Parque Tecnológico Ciencias de la Salud, Granada, Spain M. F. González-Escribano Servicio de Inmunología, Hospital Universitario Virgen del Rocío (IBiS, CSIC, US), Sevilla, Spain e-mail: [email protected] © Springer Nature Switzerland AG 2019 J. Martín, F. D. Carmona (eds.), Genetics of Rare Autoimmune Diseases, Rare Diseases of the Immune System, https://doi.org/10.1007/978-3-030-03934-9_3

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being the most widely used those proposed by the International BD Study Group (ICBD) in 1990 [2]. BD is found worldwide; however, its prevalence varies along the different geographical regions. The highest prevalence is found in Turkey, followed by Japan and Iran, and it is very low in North America and in Western countries; in fact, BD is also known as “Silk Road disease” given its particular geographical distribution overlap with the ancient trading route stretching from China to the Mediterranean area [3]. Regarding the gender distribution and onset, both genders are affected equally, although with geographical differences, and the disease typically arises in the third or fourth decade of life, being uncommon in children or patients above 50s [4]. With respect to the immunological data, multiple alterations have been described in the homeostasis of the T cells in BD patients. Accordingly, activation of γ/δ T lymphocytes in both peripheral blood and mucous lesions has been described [5, 6]. Besides, imbalances in T helper (Th) cell populations have been extensively studied in BD, and Th1 infiltrates have been observed in oral and genital ulcers and cutaneous and gastrointestinal lesions. Consistently, an increase in Th1 cytokines has also been found in blood [7–9]. In addition, high levels of IL23 and IL17 have been described in peripheral blood mononuclear cells of BD patients [10]. IL23 induces the production of IL17 by T lymphocytes, and this cytokine promotes a neutrophil-­ mediated inflammatory response. Therefore, high levels of IL23 are consistent with the hyperactivation of neutrophils observed in the early phases of the lesion infiltration [11], which leads to the increase in the levels of reactive oxygen species, endothelial adhesion, chemotaxis, and phagocytosis [12–15]. All these data suggest that the Th17/Th1 balance plays an important role in the regulation of the inflammation in BD [16]. Despite high efforts, the etiology of BD remains unclear. However, cumulative evidences suggest that certain infectious agents and environmental factors may trigger the disease in genetically predisposed individuals. It has been proposed that different virus and bacteria could play a role in the BD development. Of special note are Herpes simplex virus I, which DNA has been isolated from genital ulcers and saliva samples of patients [17], and Streptococcus sanguinis that has been related with the formation of recurrent aphthous lesions [18]. Nevertheless, the present chapter focuses on the genetic component, in which great advances have been made in recent years.

3.2

Genetic Component of Behçet’s Disease

The substantial genetic contribution to the pathogenesis of this disease is strongly supported by several facts. In addition to the aforementioned geographical variation in prevalence [3], familial aggregation has been extensively reported. The results of these studies revealed a higher frequency of the cases among the relatives of the patients than in the general population [19–22] with the highest sibling recurrence risk ratio, in the Turkish population (between 11.4 and 52.5) [20]. Besides, although there are few studies in BD, the disease concordance rate was higher in

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monozygotic twins compared with dizygotic ones [23]. Finally, specific associations of different genes with BD susceptibility have been robustly described [24]. From a genetic point of view, BD is considered as a complex disease, in which multiple genes are involved, each of them with a modest effect in the disease, being able to be related with the onset as well as with its severity and progression. We will examine throughout this chapter the current knowledge of the BD genetic background on the basis of the recent advances in this subject. Firstly, the HLA region (which represents the main genetic contributor to the disease) will be thoroughly reviewed together with the main confirmed associated loci outside the HLA region. Besides, we summarized the results of other studies that propose new susceptibility genetic factors, and, finally, we will highlight the most important molecular pathways implicated in the disease.

3.2.1 HLA Region The major histocompatibility complex (MHC) region includes the largest number of genetic associations for a wide range of pathological conditions, including most of the immune-mediated diseases. The earliest association of BD with the human MHC (HLA) was reported in the 1970s [25]. These initial studies, using serological typing method, revealed that HLA-B51 had a relevant relationship with the disease, so that it was detected with a relative small sample size. Decades later, with the availability of DNA-based methods and larger study cohorts, the specific association of HLA-B*51 with the disease was well established and repeatedly contrasted in different ethnic groups [24]. Multiple studies exploring other additional susceptibility loci in the HLA region have suggested the association of diverse HLA molecules with the disease. In this sense, in addition to other HLA-B molecules [26, 27], some studies have reported other classic class I HLA molecules (HLA-A and HLA-­C) as associated to the disease, although in general these results were less consistent [28–30]. As we stated above, the association between HLA-B*51 and BD is well established worldwide as the strongest genetic risk factor for this condition, but the functional basis of this association in the pathogenesis of BD has not been elucidated yet. For this reason, other loci located within the HLA region have been proposed as major contributors. Specifically, a study published in 1999 in the Japanese population proposed for the first time that the causative gene of the HLA region was MICA, a gene located very close to HLA-B with strong linkage disequilibrium (LD) [31–33]. Although this idea was embraced by the scientific community at the beginning, later studies performed in different populations were inconsistent [34–38]. The problems encountered to clarify which gene is true responsible of the association are related to the complexity of the HLA region, which is particularly dense in genes related with the immune system and shows a strong LD, making difficult to clarify whether the identified risk signals are independent each other or whether they are reflections of the primary association. This problem is aggravated in the case of rare diseases, such as BD, because

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the sample size, relatively low compared with other pathologies with more prevalence, limits the statistical power of the individual studies. Given the advance of the new typing approaches during the last years, new data have been raised from fine-mapping studies, thus allowing the typing of the entire region. Four independent studies performed in different populations, and following a comprehensive approach that combines high-throughput genomics with the novel algorithms of HLA imputation [39, 40], have been published [41–44]. All of these studies concluded that the HLA-B*51 haplotype represents the strongest association factor with the disease but also discarded an association of any HLA class II molecule, something that was previously proposed using older methodologies [41–44]. However, these studies showed discrepancies regarding the independent association of other HLA class I factors. In the context of the HLA-B locus, HLAB*15 and HLA-B*27 were identified as risk factors and HLA-B*49 as a protective factor with independent effects of HLA-B*51 in one study in the Turkish population [42]. Additionally, the allelic group HLA-B*57 was shown to confer susceptibility to BD in two studies conducted in the Spanish and Turkish populations [42, 43]. With regard to the HLA-A locus, variation within the gene was also associated with disease predisposition in different studies. Specifically, three of these studies found that the HLA-A*03 group is an independent protective factor for BD [42– 44]. In addition, one of these studies reported suggested evidence that HLA-A*26 could be also an independent risk factor for BD [42]. Only one study described an independent association of the HLA-C locus, specifically HLA-C*1602 [41], which had been previously suggested by two smaller studies from Southern European [45, 46]. However, it should be noted that this independent association of the HLAC haplotype was not consistent with the rest of the studies that evaluated the contribution of this locus [26, 42–44]. On the other hand, although the results of one of these studies returned the idea of the MICA gene as the causal susceptibility marker for BD [41], the results of the other large-scale genetic studies did not support an independent association of the MICA gene with BD [34–38, 42, 43]. Many of the HLA class I molecules have a dual function, and they present peptides to the CD8 T cells, but they also control the activity of the natural killer cells because they are ligands of some of their receptors (KIR). The relevant amino acid positions in one or the other function are located in different parts of the molecule. Therefore, deciphering the amino acid positions involved in BD susceptibility may definitively help to better understand the functional implications of the HLA system in the disease pathophysiology. In an effort to identify the motifs that may explain the variety of protective and risk effects conferred by HLA class I molecules in BD, the possible association of polymorphic amino acid residues has been analyzed in some studies [41, 43]. One of them proposed a model comprising five amino acids located in three positions of HLA-B, 67 (glutamic acid, Glu, or phenylalanine, Phe), 97 (threonine, Thr), and 116 (leucine, Leu), as well as one position in HLA-A, 161 (Glu) [41]. According to one of those studies in which an omnibus test was performed, the most relevant amino acid position for disease risk was HLA-B 97. Six possible residues can be harbored at this position, with two of them conferring

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risk (Thr and valine, Val), another two conferring protection (arginine, Arg, and serine, Ser), and the remaining two being neutral (asparagine, Asn, and tryptophan, Trp). In addition, according to this model, the position 66 of HLA-A represented an independent association (with lysine, Lys conferring risk and Asn protection) [43]. Interestingly, the four more relevant positions (HLA-B 67, 97, 116 and HLA-A 66) are located in the binding groove of their corresponding molecules. These data brought additional evidences supporting the importance of the peptide binding by the class I HLA molecules in BD.  Nevertheless, the study of the most relevant amino acid positions has to be interpreted with caution, and it is evident that each HLA-B molecule has a specific set of amino acids in its polymorphic positions, with many of them in LD with each other, thus increasing the difficulty to evaluate dependency at the amino acid level. Therefore, it should be noted that dependency does not exclude biological influence. Therefore, only by complementing the knowledge gained by this type of approaches with those provided by functional studies, it would be possible to elucidate the precise etiopathogenic role of these molecules in disease, which would be essential for a personalized medicine [43].

3.2.2 Non-HLA Region The contribution of the HLA region to the genetic component of BD has been estimated to be approximately 20% [47], which indicates that other genes outside the region may have to be involved in this pathology. For many years, a large number of candidate gene studies tried to unravel the complex genetic architecture of BD outside of the HLA region. However, those studies yielded contradictory results that were not usually replicated in independent populations. The identification of the genetic factors involved in the susceptibility to complex diseases represents an enormous challenge, given that the effect of each gene in the development of this type of diseases is relatively low independently. To detect genes with low or medium effects, very large sample sizes are needed, and, given the condition of BD as a rare disease, most of candidate gene studies performed did not have enough statistical power. Additionally, the lack of replication among studies could be also related to specific population associations due to particular genetic architectures. Due to the above, few consistent associations with BD have been identified to date (Table 3.1).

3.2.2.1 Confirmed Risk Loci Interleukin 23 receptor (IL23-R). This gene represents the most consistently associated non-HLA locus with BD, as it has been repeatedly identified as a risk factor for this disease in different populations and multiple independent studies [26, 43, 44, 48–56]. It is worth mentioning that this gene is a known risk factor for a multitude of immune-related diseases [57–59]. IL23R encodes a subunit of the IL-23 receptor which is expressed on the surface of Th17 cells and macrophages and binds the subunit p19 of IL-23, a pro-inflammatory cytokine composed by p19 and p40 (which is

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Table 3.1  Confirmed risk loci for Behçet’s disease outside the HLA region Locus IL23-R

Location 1p31.3

Gene name Interleukin 23 receptor

IL-10

1q32.1

Interleukin 10

IL12A

3q25.33

Interleukin 12A

STAT4

2q32.2-q32.3

Signal transducer and activator of transcription-4

ERAP1

5q15

Endoplasmic reticulum aminopeptidase 1

SNP rs1495965 rs924080 rs10889664 rs1518110 rs1518111 rs1800871 rs17810546 rs1874886 rs7574070 rs7572482 rs897200 rs17482078 rs10050860 rs2287987

FUT2

19q13.33

Fucosyltransferase 2

rs13154629 rs681343 rs601338 rs602662

KLRC4

12p13.2

Killer cell lectin-like receptor C4

rs632111 rs2617170 rs1841958

CCR1-­ CCR3

3p21.31

C-C motif chemokine receptor

rs7616215 rs13084057 rs13092160 rs13075270

SNP function Intergenic Intergenic Intergenic Intronic Intronic Intronic Intergenic Intergenic Intronic Intronic Intergenic Missense (Arg725Gln) Missense (Asp575Asn) Missense (Met349Val) Intronic Synonymous Missense (Trp143Ter) Missense (Ser258Gly) 3′-UTR Missense (Asn104Ser) Missense (Ile129Ser) Intergenic Intergenic Exonic Exonic

References [26, 43, 44, 48–56] [26, 44, 48–51, 55, 61] [43, 44, 55, 67, 68] [53, 55, 68, 71] [55, 68, 74, 75]

[44, 104]

[55, 68]

[68, 90]

SNP single-nucleotide polymorphism

common with IL-12 and binds to IL12RB1). The IL-23/IL-23R complex promotes the polarization of the T cells to Th17 and increases the levels of inflammatory cytokines such as IL-1, IL-6, IL-17, and TNFα [60]. Although there are strong evidences supporting that the variants of IL23R influence BD susceptibility through their effects on IL-23R itself, an additional role of these variants as markers of other nearby genes, such as IL12RB2, cannot be discarded [49]. IL12RB2 gene encodes the IL-12 receptor beta 2, and the complex IL-12/IL12RB2 has a crucial role in Th1 cell differentiation. Thus, although the causal mechanism of the association remains unclear, all these evidences support an important role of the IL-23/IL-17 pathway in the pathophysiology of immune-mediated diseases, including BD.

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Interleukin 10 (IL10). This gene was identified together with IL23R as susceptibility factor for BD, and this association has been subsequently replicated in different populations [26, 44, 48–51, 55, 61, 62]. IL-10 is a potent anti-inflammatory molecule that inhibits the activation of macrophages and the synthesis of pro-­ inflammatory cytokines (including IL-1, IL-6, and TNFα); therefore, it suppresses Th1 cell activation [63]. Imbalances in the regulation of Th1 activation could cause deviations toward a Th1 profile, which could predispose to the disease. Interestingly, several genetic variants within this gene have been associated with the levels of expression. Specifically, the SNP reported by the Remmers group is associated with a decrease of the IL10 expression levels in monocytes [49, 64, 65]. Besides, it has been demonstrated that a low expression of IL10 in mouse leads to inflammation processes [66]. Interleukin 12A (IL12A). This gene encodes a subunit of IL-12, a cytokine that plays an important role in the polarization of the immune response toward Th1 and also in the production of IFNγ by both the T lymphocytes and the NK cells, so it is related to the production of pro-inflammatory cytokines [67]. Several studies reported association of this gene with BD [43, 44, 55, 68, 69], although further investigation is needed to clarify the causal variant. The signal transducer and activator of transcription-4 (STAT4). This gene represents a shared genetic susceptibility factor for several autoimmune diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and Sjögren’s syndrome, among others [70, 71]. Regarding BD, the association with this gene has been extensively reported in different populations [53, 55, 69, 72]. STAT4 is a transcription factor that is activated by cytokines such as IL-12 and IL-23, which, as stated above, are involved in the differentiation of lymphocytes into Th1 and Th17 [73]. Two of the identified risk variants of this gene have been implicated in changes of STAT4 mRNA expression [69, 74], although further experiments are needed to better understand the way in which this genetic variation affect the pathogenesis of BD. The endoplasmic reticulum aminopeptidase 1 (ERAP1). The association of a missense variant of ERAP1, p.Arg725Gln, was described for the first time by Kirino and colleagues, whose data suggested that the associated variant contributes to disease susceptibility through a strong interaction or epistasis with HLA-B51 [69]. After that, additional studies have replicated these first results [55, 75, 76]. It is noteworthy that the association of this gene with other HLA class I-related diseases such as ankylosing spondylitis (AS) and psoriasis has been thoroughly investigated in the last years, and the implication of ERAP1 with these diseases has been always reported through an epistatic interaction with the corresponding associated HLA allele in each case [57, 77–79]. This gene encodes an aminopeptidase with an ubiquitous distribution, which plays an important role trimming the N-terminal end of the peptides in the endoplasmic reticulum, a critical step of the processing of the peptides to optimize their length for HLA I molecule binding [80]. Fucosyltransferase 2 (FUT2). This association was firstly reported by Xavier and collaborators [81] but also in a recently published large-scale study [44]. This gene encodes the alpha [1, 2] fucosyltransferase, a molecule that produces in fluids

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and intestinal mucosa the secreted H antigen, which is the precursor of the ABO histo-blood group antigens [82]. It has been described that homozygosity in two missense variants (p.Trp143Ter and p.Ile129Phe) leads to an ABO nonsecretor phenotype [82]. Of note, these variants are also linked with other immune-mediated disorders such as Crohn’s disease and type 1 diabetes [83–85]. The nonsecretor phenotype has also been associated with resistance to several infectious agents [86, 87] and the gut microbiome composition [88, 89]. These evidences support the hypothesis that relationship between infectious agents and the genetic component is crucial for the development of BD. Killer cell lectin-like receptor C4 (KLRC4). This gene has been found as a susceptibility locus in two large-scale genetic studies. Two non-synonymous variants in high LD (p.Ile29Ser and p.Asn104Ser) seem to be part of the susceptibility haplotype for BD [55, 69]. The KLRC4 gene, also known as NKG2F, encodes a c-type lectin receptor expressed on NK cells. Although the specific function of this molecule is unknown, the haplotype related with the disease has reported to be associated with a high natural cytotoxic activity on peripheral blood cells [90]. CCR1-CCR3. This locus harbors a cluster of chemokine receptor genes with a high LD among them [69, 91]. Through binding to its ligands, these receptors act as a key regulator in leukocyte trafficking and in the homeostasis of the immune system [92]. The risk allele reported by Hou et al. has been associated with a reduced expression of both, CCR1 and CCR3, in peripheral blood mononuclear cells (PBMCs) [91] and another variant located in the same region was also related with a lower expression of CCR1 in human primary monocytes [69].

3.2.2.2 Suggested Risk Loci The number of genes identified in large-scale genetic studies is higher than that exposed in the previous section, and includes genes such as KIAA1529, CPVL, LOC100129342, UBASH3A, UBAC2 [93], GIMAP [94], JRKL-CNTN5 [43], IL1A-­IL1B, IRF8, CEBPB-PTPN1 [44]. However, the association of these loci with BD remains unconfirmed. In some cases, specific replication studies have been performed in other populations but the results obtained are contradictories [26, 95]. In other cases the association has recently been described in only one population [44]. New approaches such as next-generation sequencing (NGS) have being recently implemented for the investigation of the rare polymorphisms. In a recent study, 21 candidate genes were evaluated for BD association through deep exonic resequencing with the aim of identifying low-frequency non-synonymous variants [56]. The association of rare variants in four genes (IL23R, NOD2, TLR4, and MEFV) with BD is supported by the results obtained in this work. In a later study, seven genes related with immune-mediated diseases were analyzed using NGS. The findings of this second study suggested the influence of rare variants of, at least, NOD2, PSTPIP1, and MVK in the pathogenesis of BD [96]. More independent studies performed in other populations and/or with other approaches are necessary to confirm or discard these suggested associations.

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Molecular Pathways

In the last years, large achievements have been accomplished to understand the genetic basis of BD. Although multiple studies will still be necessary for a fully comprehension of the pathophysiology underlying this disorder, given the last advances, we can outline a model that integrates the main molecular pathways involved in the development of this disease. As it is described before, several functional studies have established that the T lymphocytes are the most important cell population involved in the immunopathogenesis of BD. These data are in concordance with the results yielded by genetic studies, because several of the BD susceptibility genes (e.g., STAT4, IL12, IL23R) are involved in the differentiation of naïve CD4+ T cells into mature Th1 effector cell or in the maintenance of Th17 cells [97] and others in the balance of Th1 cells (IL10). In addition, the association of the IL-23/IL-17 pathway with BD is also supported by genetic data and provides evidences of the essential role that this pathway has in the pathophysiology of multiple immune-mediated diseases, especially BD. The genetic association of ERAP1, FUT2, and KLRC4 supports the hypothesis that the disease would be triggered by environmental agents in which microorganisms would play a key role. On this sense, the association of FUT2 could be related with the immune response to invasive microorganisms and the microbiota composition.

3.4

Conclusions and Future Perspectives

Despite the impressive increase in our knowledge of the genetic basis of BD during the last years, the list of the confirmed risk loci for this type of vasculitis remains significantly lower than other immune-mediated diseases [98, 99]. One of the main limitations in the genetic study of this disease is the lack of statistical power, which is conditioned by the low prevalence of this disorder and that does not permit to identify susceptibility signals with modest effects for which large sample size is required. Therefore, additional strategies are necessary to unravel the genetic component underlying BD. In this sense, one new approach which is been successfully applied is the combination of the genetic data from different diseases with similar features considering them as a single phenotype (cross-phenotype meta-analysis), and numerous shared genetic components have been described in the last years using this methodology [100–102]. On the other hand, the way in which the information of genetics variants is translated into pathogenetic mechanisms remains unclear for most of the variants associated with BD, which are located mostly in noncoding region, as occurs in many immune-mediated diseases. This fact suggests that these variants could affect different regulatory elements in the genome. Thus, further studies should be focused on the effects that the associated variants produce. In this sense, the role of epigenetics in the pathogenesis of immune-mediated diseases seems now undeniable, and the contribution of epigenetic dysregulation in vasculitis is increasingly recognized. The genetic-epigenetic relationships are taking on great importance in a field in

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which functional data are emerging [103]. Expanding our knowledge of how these epigenetic mechanisms interact with the polymorphisms will help to better understand the pathogenesis of this disease.

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57. Cargill M, Schrodi SJ, Chang M, Garcia VE, Brandon R, Callis KP, et al. A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet. 2007;80(2):273–90. 58. Duerr RH, Taylor KD, Brant SR, Rioux JD, Silverberg MS, Daly MJ, et  al. A genome-­ wide association study identifies IL23R as an inflammatory bowel disease gene. Science. 2006;314(5804):1461–3. 59. Rueda B, Orozco G, Raya E, Fernandez-Sueiro JL, Mulero J, Blanco FJ, et al. The IL23R Arg381Gln non-synonymous polymorphism confers susceptibility to ankylosing spondylitis. Ann Rheum Dis. 2008;67(10):1451–4. 60. Iwakura Y, Ishigame H.  The IL-23/IL-17 axis in inflammation. J Clin Invest. 2006;116(5):1218–22. 61. Wu Z, Zheng W, Xu J, Sun F, Chen H, Li P, et  al. IL10 polymorphisms associated with Behçet’s disease in Chinese Han. Hum Immunol. 2014;75(3):271–6. 62. Kang EH, Choi JY, Lee YJ, Lee EY, Lee EB, Song YW. Single nucleotide polymorphisms in IL-10-mediated signalling pathways in Korean patients with Behçet’s disease. Clin Exp Rheumatol. 2014;32(4 Suppl 84):S27–32. 63. Wallace GR, Kondeatis E, Vaughan RW, Verity DH, Chen Y, Fortune F, et al. IL-10 genotype analysis in patients with Behçet’s disease. Hum Immunol. 2007;68(2):122–7. 64. Temple SE, Lim E, Cheong KY, Almeida CA, Price P, Ardlie KG, et  al. Alleles carried at positions -819 and -592 of the IL10 promoter affect transcription following stimulation of peripheral blood cells with Streptococcus pneumoniae. Immunogenetics. 2003;55(9):629–32. 65. Turner DM, Williams DM, Sankaran D, Lazarus M, Sinnott PJ, Hutchinson IV. An investigation of polymorphism in the interleukin-10 gene promoter. Eur J Immunogenet. 1997;24(1):1–8. 66. Moore KW, de Waal Malefyt R, Coffman RL, O’Garra A. Interleukin-10 and the interleukin­10 receptor. Annu Rev Immunol. 2001;19:683–765. 67. Chang JT, Shevach EM, Segal BM. Regulation of interleukin (IL)-12 receptor beta2 subunit expression by endogenous IL-12: a critical step in the differentiation of pathogenic autoreactive T cells. J Exp Med. 1999;189(6):969–78. 68. Kappen JH, Medina-Gomez C, van Hagen PM, Stolk L, Estrada K, Rivadeneira F, et  al. Genome-wide association study in an admixed case series reveals IL12A as a new candidate in Behçet disease. PLoS One. 2015;10(3):e0119085. 69. Kirino Y, Bertsias G, Ishigatsubo Y, Mizuki N, Tugal-Tutkun I, Seyahi E, et  al. Genome-­ wide association analysis identifies new susceptibility loci for Behçet’s disease and epistasis between HLA-B*51 and ERAP1. Nat Genet. 2013;45(2):202–7. 70. Remmers EF, Plenge RM, Lee AT, Graham RR, Hom G, Behrens TW, et  al. STAT4 and the risk of rheumatoid arthritis and systemic lupus erythematosus. N Engl J Med. 2007;357(10):977–86. 71. Korman BD, Alba MI, Le JM, Alevizos I, Smith JA, Nikolov NP, et al. Variant form of STAT4 is associated with primary Sjögren’s syndrome. Genes Immun. 2008;9(3):267–70. 72. Hou S, Yang Z, Du L, Jiang Z, Shu Q, Chen Y, et al. Identification of a susceptibility locus in STAT4 for Behçet’s disease in Han Chinese in a genome-wide association study. Arthritis Rheum. 2012;64(12):4104–13. 73. Morinobu A, Gadina M, Strober W, Visconti R, Fornace A, Montagna C, et al. STAT4 serine phosphorylation is critical for IL-12-induced IFN-gamma production but not for cell proliferation. Proc Natl Acad Sci U S A. 2002;99(19):12281–6. 74. Abelson AK, Delgado-Vega AM, Kozyrev SV, Sánchez E, Velázquez-Cruz R, Eriksson N, et al. STAT4 associates with systemic lupus erythematosus through two independent effects that correlate with gene expression and act additively with IRF5 to increase risk. Ann Rheum Dis. 2009;68(11):1746–53. 75. Conde-Jaldón M, Montes-Cano MA, García-Lozano JR, Ortiz-Fernández L, Ortego-Centeno N, González-León R, et al. Epistatic interaction of ERAP1 and HLA-B in Behçet disease: a replication study in the Spanish population. PLoS One. 2014;9(7):e102100.

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76. Takeuchi M, Ombrello MJ, Kirino Y, Erer B, Tugal-Tutkun I, Seyahi E, et al. A single endoplasmic reticulum aminopeptidase-1 protein allotype is a strong risk factor for Behçet’s disease in HLA-B*51 carriers. Ann Rheum Dis. 2016;75(12):2208–11. 77. Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, et  al. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants. Nat Genet. 2007;39(11):1329–37. 78. Strange A, Capon F, Spencer CC, Knight J, Weale ME, Allen MH, et al. A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1. Nat Genet. 2010;42(11):985–90. 79. Sun LD, Cheng H, Wang ZX, Zhang AP, Wang PG, Xu JH, et  al. Association analyses identify six new psoriasis susceptibility loci in the Chinese population. Nat Genet. 2010;42(11):1005–9. 80. Saric T, Chang SC, Hattori A, York IA, Markant S, Rock KL, et al. An IFN-gamma-induced aminopeptidase in the ER, ERAP1, trims precursors to MHC class I-presented peptides. Nat Immunol. 2002;3(12):1169–76. 81. Xavier JM, Shahram F, Sousa I, Davatchi F, Matos M, Abdollahi BS, et al. FUT2: filling the gap between genes and environment in Behçet’s disease? Ann Rheum Dis. 2015;74(3):618–24. 82. Ferrer-Admetlla A, Sikora M, Laayouni H, Esteve A, Roubinet F, Blancher A, et al. A natural history of FUT2 polymorphism in humans. Mol Biol Evol. 2009;26(9):1993–2003. 83. Franke A, McGovern DP, Barrett JC, Wang K, Radford-Smith GL, Ahmad T, et al. Genome-­ wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat Genet. 2010;42(12):1118–25. 84. Hu DY, Shao XX, Xu CL, Xia SL, Yu LQ, Jiang LJ, et al. Associations of FUT2 and FUT3 gene polymorphisms with Crohn’s disease in Chinese patients. J Gastroenterol Hepatol. 2014;29(10):1778–85. 85. Smyth DJ, Cooper JD, Howson JM, Clarke P, Downes K, Mistry T, et  al. FUT2 nonsecretor status links type 1 diabetes susceptibility and resistance to infection. Diabetes. 2011;60(11):3081–4. 86. Lindesmith L, Moe C, Marionneau S, Ruvoen N, Jiang X, Lindblad L, et al. Human susceptibility and resistance to Norwalk virus infection. Nat Med. 2003;9(5):548–53. 87. Ruiz-Palacios GM, Cervantes LE, Ramos P, Chavez-Munguia B, Newburg DS. Campylobacter jejuni binds intestinal H(O) antigen (Fuc alpha 1, 2Gal beta 1, 4GlcNAc), and fucosyloligosaccharides of human milk inhibit its binding and infection. J Biol Chem. 2003;278(16):14112–20. 88. Wacklin P, Mäkivuokko H, Alakulppi N, Nikkilä J, Tenkanen H, Räbinä J, et  al. Secretor genotype (FUT2 gene) is strongly associated with the composition of bifidobacteria in the human intestine. PLoS One. 2011;6(5):e20113. 89. Rausch P, Rehman A, Künzel S, Häsler R, Ott SJ, Schreiber S, et  al. Colonic mucosa-­ associated microbiota is influenced by an interaction of Crohn disease and FUT2 (Secretor) genotype. Proc Natl Acad Sci U S A. 2011;108(47):19030–5. 90. Hayashi T, Imai K, Morishita Y, Hayashi I, Kusunoki Y, Nakachi K.  Identification of the NKG2D haplotypes associated with natural cytotoxic activity of peripheral blood lymphocytes and cancer immunosurveillance. Cancer Res. 2006;66(1):563–70. 91. Hou S, Xiao X, Li F, Jiang Z, Kijlstra A, Yang P. Two-stage association study in Chinese Han identifies two independent associations in CCR1/CCR3 locus as candidate for Behçet’s disease susceptibility. Hum Genet. 2012;131(12):1841–50. 92. Marzio PD, Sherry B, Thomas EK, Franchin G, Schmidtmayerova H, Bukrinsky M. beta-­ Chemokine production in CD40L-stimulated monocyte-derived macrophages requires activation of MAPK signaling pathways. Cytokine. 2003;23(3):53–63. 93. Fei Y, Webb R, Cobb BL, Direskeneli H, Saruhan-Direskeneli G, Sawalha AH. Identification of novel genetic susceptibility loci for Behçet’s disease using a genome-wide association study. Arthritis Res Ther. 2009;11(3):R66.

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94. Lee YJ, Horie Y, Wallace GR, Choi YS, Park JA, Choi JY, et al. Genome-wide association study identifies GIMAP as a novel susceptibility locus for Behcet’s disease. Ann Rheum Dis. 2013;72(9):1510–6. 95. Ortiz-Fernández L, Conde-Jaldón M, García-Lozano JR, Montes-Cano MA, Ortego-Centeno N, Castillo-Palma MJ, et  al. GIMAP and Behçet disease: no association in the European population. Ann Rheum Dis. 2014;73(7):1433–4. 96. Burillo-Sanz S, Montes-Cano MA, García-Lozano JR, Ortiz-Fernández L, Ortego-Centeno N, García-Hernández FJ, et al. Mutational profile of rare variants in inflammasome-related genes in Behçet disease: a next generation sequencing approach. Sci Rep. 2017;7(1):8453. 97. Kastelein RA, Hunter CA, Cua DJ. Discovery and biology of IL-23 and IL-27: related but functionally distinct regulators of inflammation. Annu Rev Immunol. 2007;25:221–42. 98. Carmona FD, González-Gay MA, Martín J.  Genetic component of giant cell arteritis. Rheumatology (Oxford). 2014;53(1):6–18. 99. Eyre S, Orozco G, Worthington J.  The genetics revolution in rheumatology: large scale genomic arrays and genetic mapping. Nat Rev Rheumatol. 2017;13(7):421–32. 100. Ortiz-Fernández L, Carmona FD, López-Mejías R, González-Escribano MF, Lyons PA, Morgan AW, et al. Cross-phenotype analysis of Immunochip data identifies. Ann Rheum Dis. 2018;77(4):589–95. 101. Ellinghaus D, Jostins L, Spain SL, Cortes A, Bethune J, Han B, et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat Genet. 2016;48(5):510–8. 102. Carmona FD, Coit P, Saruhan-Direskeneli G, Hernández-Rodríguez J, Cid MC, Solans R, et al. Analysis of the common genetic component of large-vessel vasculitides through a meta-­ Immunochip strategy. Sci Rep. 2017;7:43953. 103. Coit P, Direskeneli H, Sawalha AH. An update on the role of epigenetics in systemic vasculitis. Curr Opin Rheumatol. 2018;30(1):4–15. 104. Serwold T, Gonzalez F, Kim J, Jacob R, Shastri N. ERAAP customizes peptides for MHC class I molecules in the endoplasmic reticulum. Nature. 2002;419(6906):480–3.

4

Sjögren’s Syndrome Laëtitia Le Pottier, Kahina Amrouche, Amandine Charras, Anne Bordron, and Jacques-Olivier Pers

Contents 4.1  I ntroduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  54 4.2  Immunopathology of pSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  54 4.2.1  An Autoimmune Epithelitis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  54 4.2.2  An Interferon Signature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  56 4.2.3  The Role of BAFF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  56 4.2.4  A Form of B-Cell Hyperactivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  56 4.2.5  A Defect of the Suppressive Immune Response. . . . . . . . . . . . . . . . . . . . . . . . .  57 4.2.6  Evolution Toward Lymphoma. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  57 4.3  Genetics of Sjögren’s Syndrome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  58 4.3.1  HLA Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  58 4.3.2  Non-HLA Associations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  61 4.4  Epigenetic Mechanisms in Sjögren’s Syndrome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  70 4.4.1  Environmental Factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  70 4.4.2  Global Analysis of DNA Methylation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  71 4.4.3  Complete Analysis of Methylome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  72 4.4.4  Epigenetic Reprogramming and Its Consequences. . . . . . . . . . . . . . . . . . . . . . .  74 4.4.5  Link Between Genetic and Epigenetic Risk Factors. . . . . . . . . . . . . . . . . . . . . .  79 4.5  Genetic Alterations in Sjögren’s Syndrome-Related Lymphoma . . . . . . . . . . . . . . . . . .  80 4.5.1  Lymphomagenesis Scenario in Primary Sjögren’s Syndrome. . . . . . . . . . . . . . .  80 4.5.2  Chromosomal Translocation of MALT1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  81 4.5.3  BAFF Genetic Variants and BAFF Receptor Mutation. . . . . . . . . . . . . . . . . . . .  82 4.5.4  TNFAIP3 (A20) Inactivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  83 4.5.5  MTHFR Polymorphism in Non-MALT Lymphoma. . . . . . . . . . . . . . . . . . . . . .  84 4.6  Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  86 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  86

L. Le Pottier · K. Amrouche · A. Charras · A. Bordron · J.-O. Pers (*) Univ Brest, UMR1227, Lymphocytes B et Autoimmunité, Brest, France e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2019 J. Martín, F. D. Carmona (eds.), Genetics of Rare Autoimmune Diseases, Rare Diseases of the Immune System, https://doi.org/10.1007/978-3-030-03934-9_4

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Introduction

Primary Sjögren’s syndrome (pSS) is a systemic autoimmune disease characterized by sicca symptoms and a broad variety of systemic clinical manifestations. The prevalence in the general population is 0.02–0.1%, and middle-aged women are predominantly affected [1]. Indeed, even though keratoconjunctivitis sicca (KCS), resulting from the involvement of lacrimal glands, and xerostomia, resulting from the involvement of salivary glands (SGs), are usually prominent, pSS presents as a multifaceted and systemic condition with a broad variety of clinical manifestations. The spectrum of pSS extends from an organ-specific autoimmune disorder (referred to as an autoimmune exocrinopathy) to a systemic process that may involve the musculoskeletal system, nervous system, lungs, kidneys, and blood vessels. Biological abnormalities associated with B lymphocytes are also a hallmark of the disease, and these abnormalities are characterized by the presence of rheumatoid factor (RF), hypergammaglobulinemia, anti-sicca syndrome A/Ro (SSA) and anti-­ sicca syndrome B/La (SSB) antibodies, and an abnormal distribution of mature B lymphocytes in the peripheral blood [2], in addition to an increased risk of non-­ Hodgkin’s lymphoma (NHL) in 5% of patients [3]. More than 50 years ago, genetic involvement was suggested in the etiology of pSS [4]. The idea that genetic and epigenetic factors contribute to the etiology of systemic autoimmune diseases such as pSS is supported by familial autoimmunity and poly-autoimmunity [5]. Indeed, some studies have reported familial forms of pSS [6] with some cases in twins. However, although the number of cases remains too low to evaluate the familial risk of pSS, several studies have been conducted based on the co-aggregation of autoimmune diseases in affected families [5, 7]. The clustering of autoimmune diseases such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), autoimmune thyroid disease (AITD), psoriasis, multiple sclerosis, and pSS within families has frequently been reported. Most of the genes associated with susceptibility to pSS have been identified because the proteins involved have been previously associated with the pathogenesis of pSS or because the genes had already been associated with another autoimmune disease such as SLE or RA. Consequently, in this chapter, we will first focus on the immunopathology of pSS in order to better understand the genetic and epigenetic alterations described in the disease. The last section will be dedicated to genetic alterations in pSS related to lymphoma.

4.2

Immunopathology of pSS

4.2.1 An Autoimmune Epithelitis Lymphocytic infiltration is a histological hallmark of pSS. T and B lymphocytes indeed constitute the vast majority of the mononuclear cells infiltrating the salivary glands. Although the majority of infiltrating mononuclear cells are T cells in mild lesions, B-cell levels reach up to 50% in advanced lesions [8]. Epithelial cell

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(EC) apoptosis induced by the lymphocytic infiltrates is considered a key factor in the decreased production of exocrine secretions. Fas-FasL mechanisms may explain the increased EC apoptosis mediated by T cells, but B cells also directly mediate EC death via a mechanism that is independent of Fas-FasL interactions but requires protein kinase C delta (PKC δ) translocation into the EC nucleus [9]. Immunohistochemical studies of inflammatory salivary gland tissue from patients with pSS have shown that the ECs contain high levels of several immunoactive molecules known to mediate lymphoid cell homing, antigen presentation, and the amplification of epithelial cell-immune cell interactions. ECs can secrete proinflammatory cytokines via a mechanism initially mediated by Toll-like receptor (TLR) activation. Several TLRs are expressed by ECs in salivary gland tissue (TLR2, TLR3, TLR4, and TLR7) [10]. Consequently, the most likely hypothesis is that EC activation in pSS may be related to the viral infection of the exocrine glands. Toll-­like receptor signaling in the salivary gland ECs, which is achieved through epigenetic mechanisms [11], upregulates the expression of MHC-I, CD54/ICAM-I, CD40, CD95/Fas proteins, CD80, and CD86, thereby linking the innate and adaptive immune responses. Global DNA methylation is reduced in salivary gland ECs and could explain the aberrant transcription of many genes by these cells. Global DNA demethylation of ECs was accounted for by a decrease in the methylating enzyme DNMT1, which is associated with an increase in its demethylating partner Gadd45-­alpha [12]. Thus, environmental factors may convert ECs to nonprofessional antigen-­presenting cells (APCs) that also induce the polarization of naive T cells. Furthermore, interferon (IFN)-γ increases HLAII expression by ECs, thereby encouraging them to shift toward APCs. The functional expression of these immunoreactive molecules indicates that salivary gland ECs can probably mediate the presentation of antigen peptides and the transmission of activation signals to T cells. Furthermore, cytokines play a role in EC activation, as shown by the finding that IFN-γ and interleukin (IL)-1β induce CD40 expression by cultured ECs. Finally, nuclear autoantigens such as the Ro/ SSA and La/SSB ribonucleoproteins may translocate from the nucleus to the membrane of ECs. These ribonucleoproteins are present in apoptotic bodies, whose numbers are increased in ECs taken from patients with pSS. Thus, ECs can present antigens to T and B cells. Cathepsin S, the activity of which is upregulated in the lacrimal glands of patients with pSS [13], is involved in class II MHCmediated immune responses that promote the displacement of class II MHC molecules to the cell surface for presentation, thereby increasing antigen presentation by B cells and amplifying the autoimmune process. ECs also produce chemokines such as CXCL13, CCL19, and CCL21, which promote lymphocyte migration into the salivary glands. A relationship has been demonstrated linking CXCL13, salivary gland inflammation, and the loss of salivary function. Moreover, CXCL13 and CCL21 are directly involved in the organization of ectopic germinal centers. Finally, a link between CXCL13 and CCL11 and disease activity and lymphoma has been established [14]. Thus, by aberrantly expressing various immunoactive factors, ECs seem able to actively participate in and modulate the immune response within inflammatory lesions.

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4.2.2 An Interferon Signature Transcriptome analyses of salivary gland tissue and mononuclear cells from patients with pSS have shown the overexpression of type I IFN-induced genes, a phenomenon known as the IFN signature. In some patients, the IFN signature is associated with disease activity [15]. In addition to the increased expression of IFN-­regulated genes, IRF5 polymorphisms have been described in association with pSS.  These findings support a role on the part of IFN induction in the immunological activation seen in both the peripheral blood and the exocrine glands of patients with pSS. One of the most relevant IFN-induced genes encodes B-cell activating factor (BAFF) of the TNF ligand family. Thus, the IFN signature is associated with BAFF, the overexpression of which is a hallmark of pSS.

4.2.3 The Role of BAFF Serum BAFF levels are elevated in patients with pSS and associated with the increased production of autoantibodies such as anti-SSA, anti-SSB, and rheumatoid factor. Moreover, elevated BAFF levels have been found in the salivary glands of patients with pSS and are produced by B cells, T cells, and ECs [16]. Also, another observation that is in keeping with a role for BAFF in the pathogenesis of SS is that most lymphomas associated with pSS arise from B cells. Among patients with SS, those with lymphoma have higher serum BAFF levels as compared to those without lymphoma [17]. BAFF overexpression may allow autoreactive B cells to survive stronger autoantigen-triggered death signals and produce autoantibodies. This BAFF-mediated survival mechanism is evident in the peripheral blood B cells of patients with pSS because apoptosis was significantly decreased in B cells taken from patients with SS, indicating the antiapoptotic and survival-extending effects of BAFF.

4.2.4 A Form of B-Cell Hyperactivity In this context, B-cell hyperactivity is among the main features of pSS and acts by producing autoantibodies, cytokines, and antigen-presenting cells [18]. The presence of germinal cell-like structures in salivary gland biopsies from 25% of patients with pSS indicates that it may be a strong predictor of non-Hodgkin’s lymphoma development, although this observation deserves further investigation [19]. Interestingly, the terminal differentiation of B cells into plasma cells and memory B cells occurs within germinal centers under the supervision of T follicular helper (Tfh) cells [20]. Although the development and control of Tfh cells remain debated, IL-21 production by dendritic cells is known as a major contributor to the terminal differentiation of Tfh cells. Importantly, abnormal Tfh cell function has deleterious effects, including triggering autoimmunity. Ectopic germinal centers serve as a conduit to recruit and expand autoreactive B cells and may also contribute to the

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emergence of high-affinity autoantibodies in the salivary glands [18]. Interestingly, an increased circulating Tfh cell count and increased IL-21 production in these cells are found in patients with pSS and may predict a more severe clinical course [21]. Recently, IL-22 was reported to regulate lymphoid chemokine production and the assembly of ectopic germinal centers [22].

4.2.5 A Defect of the Suppressive Immune Response The evolution of pSS could also be associated with defects in the control of the immune response. Foxp3+ regulatory T cells (Treg) may play an important role in controlling autoimmunity. Foxp3+ Treg counts in minor salivary gland lesions in patients with SS are comparable to those in controls with non-SS sialadenitis, suggesting that the number of Foxp3+ Treg cells may not be decreased in SS. However, the counts of Foxp3+ T cells circulating in the blood correlate inversely with those of such cells infiltrating the salivary glands [23]. The fact that Treg cell counts are lower in advanced as compared to mild salivary gland infiltrates supports the view that dendritic cell-derived TGF-β induces Foxp3 in naive T cells and switches T-cell differentiation from the defective Treg cell pathway to a Th17 differentiation pathway in the presence of IL-6 [24]. Although B-cell overactivity is evident in pSS, a new category of B cells known as regulatory B cells (Breg) can blunt the development of autoimmune disorders [25]. The CD40-CD40L interaction between B and T cells is critical to the acquisition of Breg function upon Th1 differentiation through the production of IL-10, IL-35, and transforming growth factor β (TGF-β) or indoleamine 2,3-dioxygenase [26]. Various chronic inflammatory environments that can occur in pSS have been reported to induce Breg cell populations, and Breg cells seem to be efficient in controlling T-cell proliferation and Th1 differentiation in patients with pSS. Consequently, their depletion could explain the altered efficacy of B-cell depletion in pSS [27].

4.2.6 Evolution Toward Lymphoma Malignancy is the most fatal complication experienced by pSS patients, with an eightfold risk of mortality in this population as compared to the normal population. In consequence of that critical impact, a great deal of effort has been made to identify predictable biomarkers for lymphoma development in patients affected by pSS.  This research has provided several prognostic factors associated with lymphoma development in pSS patients, namely, the presence of rheumatoid factor, C4 hypocomplementemia, monoclonal gammopathy, lymphopenia, higher levels of disease activity, the abnormal presence of germinal center-­like structures within the mucosal sites of the exocrine glands [28], and, more recently, higher serum levels of FMS­-like tyrosine kinase 3 (FLT3) cytokine [29]. In addition, the risk of lymphoma establishment increases with disease duration, with a cumulative risk of 3.4% at 5 years and 9.8% at 15 years from diagnosis [30]. In contrast,

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among pSS patients, neither previous treatment for pSS nor male was associated with any effect on lymphoma occurrence [31].

4.3

Genetics of Sjögren’s Syndrome

Genome-wide association studies (GWAS) of complex diseases, such as autoimmune diseases, have been successful in identifying new loci and confirming previously described genetic variations. Moreover, the gene association mapping is complicated by the linkage disequilibrium (LD) and haplotype block presence in the human genome, blurring the identification of the causal variant in the loci. The LD is particularly important in the HLA region [32]. To better understand how genetic variations, such as single-nucleotide polymorphisms (SNPs) in loci, contribute to the disease, more recent studies combine GWAS and global gene expression (microarray or RNA-sequencing analyses) to map gene expression as a quantitative trait (expression quantitative trait loci [eQTL] mapping) [33].

4.3.1 HLA Associations Historically, the HLA region, located in chromosome 6p21.3, was the first associated with pSS [34]. It is not easy to summarize HLA association with pSS because, since 1975, the HLA nomenclature has changed and variant identification techniques have evolved. Moreover, the mapping of MHC (major histocompatibility complex) susceptibility variants is complex because (1) there are differences between populations other than pathological status; (2) there is a high and extensive LD in this region, and thus, the identification of causal and independent loci is complicated; and (3) some epistatic effects have been established between the MHC and other loci [35]. The extended MHC was divided into five subregions from telomeric to centromeric on the short arm of chromosome 6 (q21.3 region): extended class I region, class I region, class III region, class II region, and extended class II region (Fig. 4.1). The main genes in subregions I, II, and III are mapped in Fig. 4.1, and their associations with pSS are identified by arrows (GWAS analyses) or by stars (identified otherwise than by GWAS).

4.3.1.1 MHC Class I This subregion, with a spacing of 1.9  Mb, contains classical gene loci (HLA-A, HLA-B, and HLA-C) and nonclassical gene loci (HLA-E, HLA-F, and HLA-G). HLA-A An increased prevalence of HLA-A24 has been associated with pSS (P = 0.009; OR  =  3.5). The GWAS identification in Caucasians has shown that the HLAA*0101 allele frequency was higher in cases of pSS (Pmeta  =  6.74  ×  10−35;

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HLA-B

HLA-C

HLA-E

HLA-A

HLA-A

HLA-G

Chromosome 6 Class I 1.9 Mb

extended class I

LTA

LTB

NCR3

*

Extended MHC 7.6 Mb

TNF

p21.3

*

*

*

*

* Class III 0.7 Mb

class I

class II

A2

A1

B1

DO-A

B A

DO-B

B2 A3

DP

DM TAP2

A2 B2

B4 B3 B2 B1

A B9 B8 B7 B6 B5

A1 B1

DQ

DR class III

Class II 0.9 Mb

* * *

extended class II

* GWAS in han chinese

* * GWAS in caucasian

* Identification without GWAS

Fig. 4.1  Map of main genes in major histocompatibility complex (MHC) and association with Sjögren’s syndrome (SS). The extended MHC is located on short arm of chromosome 6 (p21.3) and divided in five subregions spanning about 7.6 megabases (Mb). The arrows indicate locus susceptibility to SS identified by genome-wide association study (GWAS) in the Han Chinese population (orange) or in the Caucasian population (blue). Star (*) indicates locus susceptibility to SS identified without GWAS

ORmeta  =  1.88) and that the cis-eQTL analysis was significant at rs113258639 (FDR = 5.02 × 10−11) [36]. The HLA-A*0101 allele was associated with anti-La/ SSB autoantibodies in Mexican patients with pSS (P = 0.003; OR = 4.75). Finally, the GWAS in the Han Chinese showed the most likely SNP at rs17186258 (P = 1.73 × 10−2; OR = 0.66) [37]. HLA-B The prevalence of HLA-B8 was significantly increased in patients with pSS. The GWAS in the Han Chinese showed that the most likely SNP was at rs4992474 (P = 8.21 × 10−5; OR = 1.40) [37]. The GWAS in Caucasians identified the HLA-­ B*0801 allele with increased frequency (Pmeta = 1.09 × 10−86; ORmeta = 3.27) and the HLA-B*1501 with decreased frequency in pSS cases (Pmeta  =  4.09  ×  10−8; ORmeta = 0.55) [36]. No significant cis-eQTL was associated with the HLA-B gene [36]. In Mexican patients, HLA-B*3501 was significantly associated with pSS (P = 0.004; OR = 3.70). HLA-C The association of the HLA-C gene with pSS was only identified via GWAS. In the Han Chinese population, the most likely SNP was at rs3905495 (P = 1.77 × 10−4; OR  =  0.75) [37]. In the Caucasian population, two alleles of HLA-C have been identified, HLA-C*0701 (Pmeta  =  3.67  ×  10−81; ORmeta  =  2.72) and HLA-C*0304 (Pmeta = 5.57 × 10−10; OR = 0.59), the frequencies of which were higher and lower, respectively, in patients with pSS [36].

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4.3.1.2 Class II This subregion, with a spacing of 0.9 Mb, contains classical gene loci (HLA-DP, HLA-DQ, and HLA-DR) and nonclassical gene loci (HLA-DO and HLA-DM). HLA-DR and HLA-DQ These two genes are always associated because three of their loci are in very strong LD (r2 = 0.97): HLA-DRB1, HLA-DQA1, and HLA-DQB1 [36]. In 1993, a study on HLA class II genes showed differing haplotype frequencies between Caucasian, Chinese, and Japanese populations. Thus, the HLA-DRB1*0301(-DRB3*0101)DQA1*0501-DQB1*0201 haplotype was significantly more common in Caucasian patients, the HLA-DRB1*0405(-DRB4*0101)-DQA1*0301-DQB1*0401 haplotype was significantly more common in Japanese patients, and the HLA-­ DRB1*0803-DQA1*0103-DQB1*0601 haplotype was significantly more common in Chinese patients. The frequency increases of these alleles in the Caucasian population have been confirmed by several studies on a Finnish population, a Scandinavian population [38], a Colombian population, and a French population [39]. Moreover, the GWAS in Caucasians confirmed that HLA-DQB1*0201 (Pmeta = 1.38 × 10−95; ORmeta = 3.36), HLA-DQA1*0501 (Pmeta = 8.50 × 10−94; ORmeta = 3.34), and HLA-­ DRB1*0301 (Pmeta = 2.19 × 10−74; ORmeta = 3.25) were the alleles most significantly associated with pSS [36]. Cis-eQTL analysis showed a significant association at rs112038669 in the HLA-DQA1 gene (FDR = 1.27 × 10−12) and, surprisingly, at rs114846898 in the HLA-DRB6 gene (FDR = 2.57 × 10−31). No significant eQTL has been identified in the HLA-DQB1 and HLA-DRB1 genes [36]. In the HLA-­ DQA1 gene, one SNP (rs9271588) has been identified in three GWAS analyses: one in a Caucasian population (Pmeta = 1.37 × 10−85; ORmeta = 0.41) [36] and two in a Han Chinese population (P = 9.50 × 10−12; OR = 0.58 for the first and P = 1.82 × 10−5; OR = 1.57 for the second) [37, 40]. HLA-DP Only the GWAS in Caucasians identified the HLA-DPB1*0101 allele, which was significantly higher in pSS cases as compared to controls (Pmeta  =  8.42  ×  10−19; ORmeta = 2.07) [36]. Moreover, HLA-DPB1 at rs3128917 showed a significant cis-­ eQTL (FDR = 3.23 × 10−14) [36]. In 1993, a study that compared HLA class II genes in Caucasian, Chinese, and Japanese patients with pSS showed no significant association with DPB1 alleles. TAP2 TAP2 (transporter associated with antigen processing 2) is a crucial subunit for the molecular assembly of the MHC-I complex. A mutation in exon 9 of TAP2, which leads to a methionine-to-valine substitution at codon 577, has been observed in a Japanese population. This new TAP2 allele, named TAP2*Bky2, showed an increased frequency in patients with pSS (P